Information

How different are tissue-specific fibroblasts from each other?


I am planning to utilize a new system in our lab, in which I will co-culture cancer cells from different tissues with fibroblasts. I have the option to receive skin-derived primary fibroblasts. I have been told the normal fibroblasts are very similar all over the body. Is this really true? I assume that fibroblasts secrete factors, for example, that are tissue specific. The question is, how quickly do they adapt to a new environment? For example if I take these skin-derived fibroblasts and co-culture them with breast cancer cells, how long will it take the fibroblasts, if any, to become more like fibroblasts derived from the breast?


I have been told the normal fibroblasts are very similar all over the body. Is this really true?

No.

Strong evidence indicates that fibroblasts in different parts of the body are intrinsically different, and there may be differences between them even in a single region [3].

They have morphological similarities, but they act differently (at least in cultures):

Examination of skin and lung fibroblasts derived from the same human fetuses revealed significant in vitro differences. Fetal lung cultures had faster cell replication rates, greater [$^3H$]thymidine incorporation into DNA, higher cell numbers at confluency, smaller cell volumes, decreased cellular RNA and protein contents and lengthened in vitro life spans when compared with fetal skin cultures. In addition, these cell cultures had different responses to the addition of hydrocortisone to culture media [1].

They hardly adapt to a different serum (in vitro) [2]. Yet they have a great capacity to differentiate (in vivo):

[… ] fibroblasts also seem to be the most versatile of connective-tissue cells, displaying a remarkable capacity to differentiate into other members of the family. [… ] “Mature” fibroblasts with a lesser capacity for transformation may, for example, exist side by side with “immature” fibroblasts (often called mesenchymal cells) that can develop into a variety of mature cell types [3].


References:

  1. E.L. Schneider, Y. Mitsui, K.S. Au, S. Stuart Shorr. Tissue-specific differences in cultured human diploid fibroblasts. Experimental Cell Research. Volume 108, Issue 1, August 1977, Pages 1-6. DOI 10.1016/S0014-4827(77)80002-5
  2. Zamansky, GLEN B., et al. "Adaptation of human diploid fibroblasts in vitro to serum from different sources." Journal of cell science 61.1 (1983): 289-297.
  3. Alberts B, Johnson A, Lewis J, et al. Molecular Biology of the Cell. 4th edition. New York: Garland Science; 2002. Fibroblasts and Their Transformations: The Connective-Tissue Cell Family. Available from: http://www.ncbi.nlm.nih.gov/books/NBK26889/

Abstract

Background

Fibroblasts are the principal stromal cells that exist in whole organs and play vital roles in many biological processes. Although the functional diversity of fibroblasts has been estimated, a comprehensive analysis of fibroblasts from the whole body has not been performed and their transcriptional diversity has not been sufficiently explored. The aim of this study was to elucidate the transcriptional diversity of human fibroblasts within the whole body.

Methods

Global gene expression analysis was performed on 63 human primary fibroblasts from 13 organs. Of these, 32 fibroblasts from gastrointestinal organs (gastrointestinal fibroblasts: GIFs) were obtained from a pair of 2 anatomical sites: the submucosal layer (submucosal fibroblasts: SMFs) and the subperitoneal layer (subperitoneal fibroblasts: SPFs). Using hierarchical clustering analysis, we elucidated identifiable subgroups of fibroblasts and analyzed the transcriptional character of each subgroup.

Results

In unsupervised clustering, 2 major clusters that separate GIFs and non-GIFs were observed. Organ- and anatomical site-dependent clusters within GIFs were also observed. The signature genes that discriminated GIFs from non-GIFs, SMFs from SPFs, and the fibroblasts of one organ from another organ consisted of genes associated with transcriptional regulation, signaling ligands, and extracellular matrix remodeling.

Conclusions

GIFs are characteristic fibroblasts with specific gene expressions from transcriptional regulation, signaling ligands, and extracellular matrix remodeling related genes. In addition, the anatomical site- and organ-dependent diversity of GIFs was also discovered. These features of GIFs contribute to their specific physiological function and homeostatic maintenance, and create a functional diversity of the gastrointestinal tract.

Citation: Higuchi Y, Kojima M, Ishii G, Aoyagi K, Sasaki H, Ochiai A (2015) Gastrointestinal Fibroblasts Have Specialized, Diverse Transcriptional Phenotypes: A Comprehensive Gene Expression Analysis of Human Fibroblasts. PLoS ONE 10(6): e0129241. https://doi.org/10.1371/journal.pone.0129241

Academic Editor: Mathias Chamaillard, INSERM, FRANCE

Received: January 19, 2015 Accepted: May 6, 2015 Published: June 5, 2015

Copyright: © 2015 Higuchi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: All relevant data, expect for raw microarray data, are within the paper and its Supporting Information files. The accession number of microarray data is GSE63626, that is avaiable in http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63626.

Funding: This work was supported by: Health and Labour Sciences Research Grants grant number 26270601 (Ministry of Health, Labour and Welfare, [http://www.mhlw.go.jp/: MK]) and a grant of a 3rd-term Comprehensive 10-year Strategy for Cancer Control grant number 23-A-3 (Ministry of Health, Labour and Welfare, [http://www.mhlw.go.jp/: AO]). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.


Background

Fibroblasts have long been considered as rather simple structural cells contributing to the extracellular matrix (ECM) in connective tissues and responsible for tissue repair during wound healing. In the last years, however, these cells have been recognized to play an important role in other processes as well, being able to exert a significant influence on all kind of cells in their environment. So, it appears now evident that fibroblasts are decisive players in inflammatory processes, modulating the activities of leukocytes by secreting specific signaling molecules at sites of inflammation [1]. Importantly, fibroblasts seem to be responsible for the termination of immune responses, or alternatively, for the switch from acute to chronic inflammation [2, 3]. During acute inflammation, immune cells are recruited and expanded in the damaged tissue. Under normal physiological conditions, control mechanisms prevent from over-stimulating these cells. Hereby fibroblasts play an important role by releasing immune-regulatory signals which promote the removal of dead or redundant immune cells. Chronic inflammation may occur when these clearance mechanisms fail. This may happen when fibroblasts either inhibit apoptotic processes by producing survival cytokines, or retain the immune cells by the release of specific chemokines [2]. Chronic inflammation processes are also involved in diseases such as fibrosis and cancer. Regulatory effects of fibroblasts on the development and progression of fibrosis and cancer have been demonstrated [4–8]. Reorganization of the ECM by fibroblasts plays thereby an important role. In tumors, additionally, interactions between fibroblasts and tumor cells seem to be essential for tumor growth and progression. These interactions are mediated through soluble signaling factors such as growth factors, cytokines, chemokines and lipid products, or by direct communication of the cells through integrins [9] ECM-degrading enzymes such as matrix metalloproteinases (MMPs), produced by fibroblasts, contribute to the degradation and remodeling of the tumor environment, thereby supporting tumor progression, including angiogenesis, invasiveness and metastasis [10].

It appears nowadays clear that fibroblasts are not only involved in structural concerns, but are also important players in patho-physiological processes. However, a lot of questions remain unanswered. So, the characterization of different fibroblast sub-types, including their expression profiles in inactivated as well as in activated and disease-related cell states, is still not clearly assessed. Up until now, no selective markers have been established, and cells with spindle-like morphology, which in-vitro adhere on plastic ware, and furthermore have non-lymphoid, non-endothelial and non-epithelial phenotypes, are generally considered as fibroblasts [1]. Some attempts have therefore been made to better characterize these cells [4, 11, 12]. However, the results of these investigations are raising new questions. In effect, it seems that more different sub-types of fibroblasts exist than expected [13]. Some researchers even claim to define new cell types and point out the fact that fibroblasts from different anatomic sites display differences which are comparable to those observed among different lineages of leukocytes [14]. The possibility cannot be excluded that multiple sub-types are co-localized at a single location. Furthermore, the origin of fibroblasts at a specific site cannot be accurately determined. They may arise from the primary mesenchyme, or alternatively from BM derived precursor cells, or from local epithelial-mesenchymal transition (EMT) [6, 15]. Knowledge about specific marker molecules, which could distinguish between fibroblasts from different anatomic sites, as well as between fibroblasts at different functional or disease-related states, would massively contribute to the understanding of patho-physiological mechanisms. Furthermore, in cancer, mortality is often related to distant metastases which rather occur as late steps of disease progression. Early cancer markers are therefore urgently needed to allow prompt treatments before aggressive forms of cancer can develop. Such early disease markers could be found in the altered stromal microenvironment of the tumor, whose main contributors are fibroblasts. Characterization of exact phenotypes of normal as well as of activated and disease-related fibroblasts would be of particular importance in this context. As different functionalities are accompanied by the synthesis or up-regulation of specific proteins, they may be revealed by expression or proteome profiling experiments.

The aim of this study was to contribute to the proteomic characterization of fibroblasts from different anatomic sites and at different functional and disease-related cell states. Proteome profiles of normal human lung fibroblasts were generated and compared to those previously obtained from primary skin and BM fibroblasts. The aim was to work out characteristic proteome signatures for these cells. Furthermore, the same cells were treated in an inflammatory way using IL-1β, in order to find out function-specific proteome alterations. Finally, in a last step we aimed to determine the functional states of tumor-associated fibroblasts from analogous tissues. On the one hand the proteome profile of lung cancer-associated fibroblasts was analyzed. On the other hand, previously published proteomic data obtained from the analysis of fibroblasts related to melanoma and thus representing cancer-associated skin fibroblasts were used, as well as fibroblasts obtained from patients with multiple myeloma, a plasma cell tumor of the BM [16, 17]. Furthermore, results obtained from our previous investigations concerning HCC-associated fibroblasts [18] were included in the present study for comparative analyses. The aim was to answer the questions if tumor-associated fibroblasts manifest an inflammatory activated cell state, and if fibroblasts related to different cancers may reveal similarities regarding their proteome profiles.


Results and discussion

One tissue type

The genes MRPL19, PSMC4, SF3A1, PUM1, ACTB and GAPD were analyzed by real-time quantitative RT-PCR. Starting copy numbers for the six candidate housekeeping genes were measured across 80 primary breast tumor samples. The data are available as Additional data file 1 with the online version of this article. Plots of the raw and log-scaled expression levels (all logarithms in this paper are natural base (e) logarithms) are shown in Figure 1. The breast tumor samples are ordered according to the mean of the log-expression levels of all the genes. It is evident from the plot that for the raw data the variability of within-sample measurements increases with the mean expression, whereas the variability stays approximately the same for all the samples with the log-transformation. In addition, the log-transformation allows us to model fold changes in expression levels in an additive way.

Relative levels of expression determined by real-time quantitative RT-PCR are shown for 6 housekeeper genes in 80 breast tumors. The top panel displays the raw data and the bottom panel displays a log-scale of the data.

To select the best housekeepers for normalizing data across a single tissue type, we tested three variations of a model (Model 1, a-c) with real-time quantitative RT-PCR data generated from primary breast samples (see Materials and methods for details).

Model 1a

We model the expression y ijof gene j in sample I by

log y ij= μ + T i+ G j+ ε ij, where ε ij

where μ is the overall mean (log-) expression, T iis the difference of the ith tissue sample from the overall average and G jis the difference of the jth gene from the overall average. The key feature of this model that makes it different from a traditional ANOVA model is that it allows heteroscedastic errors to account for different variability in the genes [17]. The variability around the gene-specific mean log-expression μ + T i+ G jis quantified by the error standard deviation σ j. The Bayesian information criterion (BIC) was used to avoid overfitting the data [18]. Model 1a had the best BIC value and was selected from a range of competing models that included a method with equal error variances (Model 1b in Materials and methods) and a more complex method with correlated errors (Model 1c in Materials and methods).

Using Model 1a, standard deviations were determined to select the best control genes for breast cancer. Table 1 shows that MRPL19 has the smallest variability across the breast cancer samples and would be the best choice for a single housekeeper control. Although some of the confidence intervals overlap, a direct comparison between the genes selected from the microarray (MRPL19, PSMC4, PUM1, SF3A1) to the classical housekeepers (GAPD and ACTB) shows significant difference (p = 0.0014).

As the biological function of many genes is still unknown, it is difficult to predict how different experimental conditions may affect the expression of putative housekeeper genes. Thus, a safer approach is to use an average expression of several genes that show small variance across conditions. On the basis of the selected model, the estimate of the variance of the log-average of the expression of several genes can be calculated (see Materials and methods for details). Table 2 shows the standard deviations of the log-average of the best gene set for each possible set size (that is, 1-6). These standard deviation values are approximately equal to the coefficient of variation in the original scale. From the estimates, the four-gene set of PSMC4, MRPL19, PUM1 and SF3A1 provides the lowest overall variability when choosing a combination of genes. However, this four-gene set is barely different from the three-gene combination of MRPL19, PUM1 and PSMC4, which in turn is far better than the best two-gene combination. For economy, and because SF3A1 had a relatively high individual variability compared to others in the set, our choice for the normalizing set is the geometric mean of the expressions of MRPL19, PUM1 and PSMC4.

These findings illustrate the importance of performing an unbiased and genome-wide search for housekeepers rather than relying on traditional housekeeper genes. We used microarray data to select genes with low variability in expression across breast tumors and cell lines. Because the quantitative differences between the microarray and RT-PCR platforms are relative, genes with low variability in expression across tumors by microarray should also show low variability in expression by RT-PCR. Although the quantitative data from microarray tends to have an overall smaller dynamic range compared to RT-PCR, this is primarily due to loss of information from genes expressed at low levels. Our microarray dataset was filtered to remove genes with signals near background noise.

The result is very similar using Vandesompele et al.'s M value method, with only the positions of PUM1 and PSMC4 changing in stability rank. It should be noted that the M-value method does not order the two best genes (MRPL19 and PSMC4). Their best gene-set selection approach would suggest using the (log-scale) average of these two best genes as a control. Such a concordance is not surprising given the close relationship between the M value and our model using the variability of the average of several genes (see Materials and methods for details). A benefit of our approach is the ability to compare the variability of individual genes to that of an average of several genes.

Multiple tissue types

Gene(s) with minimal variation in expression across different cell types serve as good 'universal' housekeepers. A universal control may be a single gene or combination of genes. While the former should display both low variability within a given tissue type and consistent basal levels of expression across tissue types, the latter may comprise a gene set with individually different, but complementary, basal expression levels across tissue types.

To test our models for selecting universal housekeepers, we used published data from Vandesompele et al. [13]. They measured the expression level of 10 genes in neuroblastoma cell lines (NB), cultured normal fibroblasts (FIB), normal leukocytes (LEU) and cells from normal bone marrow (BM). In addition, normal tissues from pooled organs (breast, brain, fetal brain, heart, kidney, uterus, lung, trachea and small intestine) were also profiled. A plot of these housekeepers across the different tissues is shown in Figure 2. It is notable that a gene can have stable expression within a given tissue type but can change rank position compared to other housekeepers across tissues. For example, GAPD has relatively high expression in fibroblasts compared to other housekeepers but low expression in leukocytes. Thus, GAPD may be a good single housekeeper within certain tissue types but may not be an optimal universal housekeeper unless it is used within a complementary gene set.

Dataset from Vandesompele et al. [13] in log-scale showing expression levels of 10 putative housekeeper genes by sample and tissue type. Tissue types analyzed included normal bone marrow (BM), cultured normal fibroblasts (FIB), normal leukocytes (LEU), neuroblastoma cell lines (NB), and pooled normal tissue from breast, brain, fetal brain, heart, kidney, uterus, lung, trachea and small intestine (POOL).

Model 2

To compare the performance of housekeepers within and between different tissues, we made a Model 2 (see Materials and methods for further details) that models the expression of gene j in the ith sample of tissue-type k by

log y i(k)j= μ + C k+ T i(k)+ G j+ (CG) kj+ ε i(k)j, where ε i(k)j

where μ denotes the overall mean (log-) expression, C kis the difference of the kth tissue type from the overall average, Ti(k) is the specific effect of the ith sample of tissue-type k, G jis the difference of the jth gene from the overall average and (CG) kjis the tissue-type specific effect of gene j. Variability in calculation comes from two sources: the specific gene (σ j) and the tissue-type (ς k). The estimates of these parameters are given in Table 3. The single gene with the overall lowest variability within each tissue type is GAPD, followed closely by UBC (ubiquitin C), HPRT1 (hypoxanthine phosphoribosyltransferase 1) and YWHAZ (tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta polypeptide). This result correlates closely with Vandesompele et al.'s approach. That is, the top five genes have exactly the same order when we rank the genes within each tissue type according to their M-value. Here we assign a rank of 1.5 to the unordered best pair and then average the ranks to obtain an overall ordering of the genes.

The risk of normalizing data to a housekeeper gene with variable overall expression level across different tissues can be represented mathematically as bias error. A housekeeper that has low bias for a particular tissue has an expression level that is near its mean expression across tissues. In our second model, the term (CG) kjrepresents this tissue-type specific bias. The measure of variability around an intended value when bias is present is called the mean squared error (MSE): MSE = bias 2 + variance. Thus, to find a set of genes for normalization across the various tissue types we use a minimax MSE criterion: minimizing the largest MSE of the combination. Table 4 provides a list for the best gene set of each size along with the minimax-MSE value. Although GAPD has relatively low overall variability within each tissue type, its basal expression changes across tissue types making it a poor choice for a single universal control. The data shows that RPL13A (ribosomal protein L13a) is the best single universal housekeeper, but it is clear that no single gene is optimal for a universal housekeeper. Actually, choosing all the candidates provides the smallest MSE, which is not surprising as the set of all 10 genes is unbiased by definition. For routine application it is reasonable to limit the number of control genes, as the cost of assaying additional genes needs to balance the extra precision obtained. With this in mind, it is instructive to note that the three-member set of HPRT1, RPL13A and UBC is an excellent choice because it maintains a priority ranking even when selection is open to including four- or five-element sets. The housekeeper genes we tested by RT-PCR on breast tumor samples were not assayed across other tissue types and thus could not be evaluated as universal controls. Nevertheless, it is likely that our results in breast tissue would hold up across other tissue types as our genes were initially selected from microarray data that included 17 different and diverse cell lines as well as primary breast tumors [19].

Figure 3 shows the MSE of each gene broken down into the squared-bias and variance components. The direction of each bar shows the sign of the bias. It is apparent that the large bias dominates the large values of MSE. The use of the (log-) average of several genes tends to reduce the variance, due to the effect of bias reduction where opposite biases cancel each other out. For example, both ACTB and TBP (TATA box binding protein) have a large bias in the pooled normal samples, but in opposing directions. The mean squared error of the (log-) average of ACTB and TBP in these samples is only 0.35, which is much lower than their individual MSEs above 6.

Mean squared error (MSE) of each gene by tissue-type. The sign is determined by the direction of the bias. The MSE is broken down into the contributing components of the squared bias (Bias 2 ) and the variance (Sigma 2 ). Dataset from Vandesompele et al. [13]. Tissue types analyzed included normal bone marrow (BM), cultured normal fibroblasts (FIB), normal leukocytes (LEU), neuroblastoma cell lines (NB), and pooled normal tissue from breast, brain, fetal brain, heart, kidney, uterus, lung, trachea and small intestine (POOL).

In summary, we have modeled the performance of putative housekeepers to test their goodness-of-fit in serving as normalization controls for relative insert quantification. A major advantage of a model approach is that the terms are placed within a solid statistical framework and are not ad hoc, which allows the algorithm to be generalized to a variety of different experimental conditions. The genes and algorithms that we have selected for normalization should have broad utility for diagnostics and research.


Discussion

Data from venerable isozyme studies show that dosage compensation in XX females is achieved through inactivation of one X chromosome in marsupial, as well as eutherian, mammals. However, unlike the random X inactivation in humans and mice, XCI was found to be paternal in all marsupial species, and at all loci tested. Observation that some genes on the paternal X are fully or partially expressed at the protein level in some kangaroo tissues led to the conclusion that marsupial XCI is incomplete and tissue specific (reviewed in [19]). It is difficult to generalize these findings to the whole X chromosome, or other marsupials, because the results are based on only three genes that were polymorphic in just one or a few marsupial species (not including our model kangaroo, the tammar wallaby).

The availability of a robust physical map of the tammar X chromosome [31], and of the tammar DNA sequence (tammar genome project, in preparation), allowed us to construct an activity map of the whole X chromosome in fibroblasts of the tammar wallaby to test the generality of the old data, and to explore outstanding questions of control of marsupial XCI at the molecular level. We used qPCR to compare the level of expression of several X-borne loci in male- and female-derived fibroblasts, finding that the female:male ratio was different for different genes, but that most genes were more highly expressed in females than in males.

Our most surprising findings were made using RNA-FISH to quantify inactivation on an individual cell basis. This method gave unique information in a species in which few polymorphisms in X-borne genes have been identified. The RNA-FISH was extremely efficient at all loci, detecting expression of 94 to 99% of loci in male cells.

Marsupial XCI is regulated at the transcriptional level

Investigations of inactivation at the protein level left open the question of whether XCI in marsupials was at the transcriptional level, as it is in eutherians [32]. The present study shows that XCI control is exerted at the transcriptional level also in marsupials, for RNA-FISH revealed that most female nuclei showed only a single signal typical of 1X-active cells. This result is confirmed by the absence of RNA polymerase from the inactive X chromosome (Chaumeil J, Waters PD, Koina E, Gilbert C, Robinson TJ & Graves JAM, submitted).

Expression from one X chromosome is coordinately controlled

Co-location of signals from neighboring genes in female fibroblast RNA-FISH experiments led us to conclude that genes are coordinately transcribed from the same active X chromosome. For instance, we found that STAG2 and PSMD10 were co-expressed in all nuclei that showed single-active expression for each locus, demonstrating that genes located close together on the same X are coordinately expressed. Pairwise comparisons using different combinations of other genes showed that all genes tested were active on the same active X chromosome, Xa. We have no way of determining the parental origin of this active chromosome, but all previous investigations on populations of cells have shown that the maternal allele is always expressed, and the inactive allele always comes from the paternal X. We therefore conclude that all alleles on the maternal X are expressed in all cells.

Expression from Xi is incomplete, and locus specific

We used RNA-FISH to examine expression of loci distributed along the tammar wallaby X chromosome. We found that all genes escaped inactivation to some extent the percent of escape from inactivation (that is, percent of 2X-active cells) for different genes varied between 5 and 68%. Each locus displays a different frequency of escape, consistent between animals, which implies that escape is locus specific. This partial, locus-specific escape confirmed the preliminary indication from qPCR data that the female:male ratio of the X gene transcript varied from complete dosage compensation to complete escape. This greatly extends the findings from isozyme studies that paternal PGK1 and G6PD are partly expressed in kangaroo fibroblasts [28, 33].

Escape from marsupial XCI is stochastic

Early studies of partial inactivation at the protein level [34] included the demonstration that single cell clones maintained the same level of paternal expression as the entire population. This was interpreted to mean that partial expression amounted to uniform down-regulation of expression of the paternal allele in all cells. Our qRT-PCR of female:male expression ratios also indicated variable degrees of transcriptional silencing in female cells. However, neither technique applied to populations of cells can distinguish between partial expression due to down-regulation of transcription from the Xi in every cell, or from different frequencies of cells with 1X-active and 2X-active expression.

Our ability to detect transcription at the level of a single nucleus using RNA-FISH therefore allowed us to discover that control is not exerted by down-regulation of the paternal allele in all cells, as had been expected. Rather, the overall level of transcription is regulated by the frequency of nuclei in which the allele on the inactive X is expressed. Regulation appears to be a stochastic (probabilistic) process since different genes show a characteristic frequency of 2X-active and 1X-active nuclei in a population of fibroblasts from the same female.

An alternative interpretation is that control of X inactivation is exerted by down-regulation of transcription from the Xi in every cell, but this low level of transcription is not detected by RNA-FISH. However, we consider that this is unlikely because RNA-FISH detects transcription in nearly 100% of loci in male cells, and DNA-FISH detects two loci in nearly all female cells. Indeed, RNA-FISH is more sensitive than DNA-FISH, in which single molecules can be detected in interphase nuclei.

Moreover, we found that genes located close together on the Xi were usually expressed at different frequencies, and in the proportions expected of independent escape from inactivation. This implies that the probabilities of transcription of different loci on the inactive X are independently regulated.

We therefore propose that regulation of escape from XCI in marsupials amounts to the control of the probability of expression of a locus on Xi, rather than of the amount of expression from the locus. Thus, expression from genes on the inactive marsupial X is under a previously unsuspected type of epigenetic control, perhaps involving locus-specific regulatory factors causing local or regional changes in chromatin organization that determine the probability that a gene on the paternal X is transcribed.

This stochastic regulation of marsupial XCI seems to be quite different from the control of XCI in mouse and human. However, although the molecular aspects of XCI have been studied in detail for the past 50 years, no comparable RNA-FISH data have been published for XCI in eutherians, and it remains possible that escape of genes on the human inactive X is stochastic. It would be very instructive to study the cell distribution of 1X- and 2X-active nuclei for genes that partially escape inactivation on the human X.

Inactivation of the marsupial X shows no polarity from an inactivation center

We constructed an activity map of the tammar wallaby inactive (presumably paternal) X in order to determine whether there was a polarity in frequency of expression. We observed no correlation between gene location and the frequency with which the allele on the Xi was expressed. Thus, there is no evidence of the polarity that was hypothesized [19] to reveal an inactivation center from which whole X chromosome control could emanate. Genes that are largely inactive were not clustered, nor were genes that largely escaped inactivation.

In addition, we found no correlation between Y expression and dosage compensation of the X paralogues. The highest frequency of escape was observed for ATRX (60%) and the lowest for RBMX (7%), both genes with Y paralogues that are not expressed in fibroblasts

RNA-FISH has the advantage that it provides information about individual cells however, it is not quantitative, and intensity of signal does not correlate with expression level. Independent studies on marsupial Y-borne genes using qPCR show that Y paralogues either show testis-specific expression or are expressed much more weakly than their X partners [35, 36] (Murtagh VJ, Sankovic N, Delbridge ML, Kuroki Y, Boore JL, Toyoda A, Jordan KS, Pask AJ, Renfree MB, Fujiyama A, Graves JAM & Waters PD, submitted).

These different expression profiles of X- and Y-borne paralogues, together with low X-Y sequence conservation (Murtagh VJ, Sankovic N, Delbridge ML, Kuroki Y, Boore JL, Toyoda A, Jordan KS, Pask AJ, Renfree MB, Fujiyama A, Graves JAM & Waters PD, submitted), suggests that Y genes have either a different or a diminished function compared with that of their X partners. Thus, the escape of these genes from XCI is unlikely to be the result of complementation by an active Y locus.

Indeed, the only feature that unites marsupial X genes with a high frequency of escape from X inactivation is that their human orthologues are located together on Xq22. Perhaps this reflects their original arrangement on an ancestral therian X 145 million years ago, at a position in which Y degradation occurred later and, therefore, XCI remains less complete.

Thus, marsupial XCI is controlled in a manner quite unlike that of the human and mouse X. In eutherians, XCI is a whole X phenomenon, in which activity domains are coordinately controlled by an inactivation center that contains the XIST gene. The independent control of the expression of loci on the inactive X is consistent with the absence of an XIST gene from the marsupial X [23, 24, 37].

Tolerance to dosage differences

XCI is widely regarded as a vital mechanism that ensures proper dosage compensation between XY males and XX females, and the initial results from older studies of XCI in humans and mice indicated that, with rare exceptions, genes on the Xi were completely inactive. This strict adherence to dosage equivalence is consistent with observations of the disastrous effects of monosomies of an autosome or autosomal region in human patients. It may therefore seem surprising that dosage compensation for many X-borne loci is incomplete or absent in marsupial fibroblasts.

However, we now know that many genes on the human X chromosome escape from inactivation [38], particularly on the short arm, which was a relatively recent addition to the X and Y chromosomes [39–41]. Even on the mouse X, which seems to represent a state of near-complete inactivation, some genes are expressed from the Xi. The first genes on the human X that were shown to be 2X-active were those that retained partners on the Y chromosome [42], suggesting that their Y partners are (or were until recently) active and complement the function of the X genes, which therefore have no need of dosage compensation. Indeed, some of the genes we studied with paralogues on the Y chromosome do escape XCI on the marsupial X (ATRX, UBA1) however, at least some Y paralogues (for example, ATRY) are testis specific and do not complement. In addition, other marsupial X genes with a Y partner, such as RBMX, PHF6X and HUWE1X, do not escape inactivation.

Perhaps, then, dosage compensation is not as critical to development and function as we had supposed. This conclusion is supported by the recent evidence that the bird Z chromosome is compensated only partially, the 934 genes on the Z showing a range of male:female dosage relationships between 1.0 and 2.0 [4, 43], and the demonstration that the five X chromosomes of the platypus (related to the bird Z and together representing more than 12% of the genome) seem to share this characteristic.

It may be that genes that require full compensation are especially sensitive to dosage effects because changes in their dose propagate through numerous downstream gene networks. Dosage differences in some genes may be critical for development of sexual differences, as is the case for the DMRT1 gene in birds [44]. In contrast, non-compensated genes may participate in intracellular housekeeping and catalytic activities that are regulated at many other levels, so their function is less sensitive to gene dosage. Such ubiquitously expressed genes are over-represented in the list of marsupial genes that largely escape inactivation.

We propose here that, during sex chromosome differentiation, the gradual loss of genes from the proto-Y chromosome selected for inactivation of the paternal allele of the homologous X-borne genes that were particularly sensitive to dosage differences in one tissue or another. This resulted in piecemeal inactivation that was tissue specific, as is observed for marsupial XCI. We suggest that the cooperative nature of the chromatin changes recruited to silence this locus in eutherians involved non-critical loci nearby. This spreading of inactivation from dosage-sensitive loci is almost complete in mouse, but has left many escaping gaps in the human X, especially on the recently recruited short arm.

Evolution of X chromosome inactivation

The fundamental difference between marsupial and eutherian XCI led us to look for similarities with dosage compensation in more distantly related mammals and non-mammal vertebrates. Indeed, the stochastic inactivation we observed in marsupials is similar to that we described recently for genes on the five X chromosomes of the platypus. X-specific genes are expressed from one or both alleles in different fibroblasts from the same female, and the frequency of 1X-active and 2X-active nuclei is a consistent feature of each gene, ranging between 20% and 53% of 2X-active nuclei [7]. However, it is hard to impute an evolutionary link between monotreme and marsupial dosage compensation since platypus X chromosomes have no homology with those of marsupials and eutherians rather, they share considerable homology with the Z chromosome of birds [10]. Dosage compensation in the chicken is known to be incomplete, ranging from a ZZ male:ZW female ratio of 1.0 to 2.0 for different genes [4]. Limited RNA-FISH was reported for five genes [5], but the low efficiency of detection makes it difficult to assess whether differences in expression represent a down-regulation in each cell, or a stochastic control of expression.

Perhaps, then, marsupial XCI retains features of an ancient silencing mechanism common to all chromosomes. The stochastic nature of marsupial and monotreme X chromosome expression is reminiscent of monoallelic expression from many autosomal loci, including olfactory receptors and immune genes such as immunoglobulins, T-cell receptors and natural-killer-cell receptors [45]. It is tempting to speculate that this reveals an ancient mechanism to control gene expression, which was exapted to evolve into an X chromosome compensation system independently in monotremes and therians [46].

A stochastic basis for transcriptional activation can be seen as a sequence of events that combines a random element, such as transcription factor binding, with a selective step, such as cell commitment. For example, a 'probability-promoting factor' identified in mouse tetraploid cells allows each X chromosome to independently determine the probability of initiating XCI [47]. The probability of inactivation of one or other X chromosome in mouse can be altered by mutations in a locus near XIST [48]. The inactivation of a single X is locked in by a feedback mechanism, controlled by the XCI center, which suppresses the inactivation of the active X [49]. Stochastic allelic expression of genes gives rise to a diverse repertoire of cells and creates diversity, so although individual cell expression profiles vary, even within a clone, the net result for a cell population will be a stable outcome.

Did an ancestral paternal, stochastic, and incomplete inactivation system, still represented by marsupials, evolve into the hyperstable chromosome-wide inactivation of eutherian mammals? The similarities of marsupial XCI with the first wave of XCI in the extraembryonic tissue of rodents and bovine (which is also paternal, incomplete and methylation independent) suggests that this represents the inactivation system in an ancient therian mammal, and it underwent changes to render it more complete and stable in eutherians. It will be very interesting to discover whether XCI in mouse embryonic membranes is, like marsupial XCI, locus specific and stochastic.

How did XCI evolve into a whole-chromosome system? The evolution of the XIST gene early in the eutherian lineage, perhaps by insertion of repetitive sequence [24] and pseudogenization of an ancient tetrapod gene[37], brought neighboring inactivation domains under chromosome-wide control. Binding with XIST RNA permitted the binding of modified histones and made DNA methylation more probable, resulting in stabilization of inactivation. Perhaps, then, stochastic expression is also the basis of random inactivation in the embryo of eutherian mammals.


Mesenchymal Stromal Cells and Tissue-Specific Progenitor Cells: Their Role in Tissue Homeostasis

Multipotent mesenchymal stromal/stem cells (MSCs) reside in many human organs and comprise heterogeneous population of cells with self-renewal ability. These cells can be isolated from different tissues, and their morphology, immunophenotype, and differentiation potential are dependent on their tissue of origin. Each organ contains specific population of stromal cells which maintain regeneration process of the tissue where they reside, but some of them have much more wide plasticity and differentiate into multiple cells lineage. MSCs isolated from adult human tissues are ideal candidates for tissue regeneration and tissue engineering. However, MSCs do not only contribute to structurally tissue repair but also MSC possess strong immunomodulatory and anti-inflammatory properties and may influence in tissue repair by modulation of local environment. This paper is presenting an overview of the current knowledge of biology of tissue-resident mesenchymal stromal and progenitor cells (originated from bone marrow, liver, skeletal muscle, skin, heart, and lung) associated with tissue regeneration and tissue homeostasis.

1. Introduction

Many human organs and tissues, including skin, liver, muscle, pancreas, lung, adipose tissue, placenta, bone marrow (BM), and peripheral blood, as well as others, contain an undifferentiated population of tissue-resident cells facilitating tissue repair and tissue remodeling during the life-time. These cells are characterized by specific properties: self-renewal capacity, the ability to give rise to descendant progenitor cells, multipotency, and the capability to differentiate into a variety of cell types specific for particular tissues. Tissue-resident stromal cells usually are localized in a specific local tissue microenvironments that maintain and control a particular type of cells or their progenitors for differentiation and maturation.

However, stromal cell function of many organs is diminished with age leading to reduced regenerative potential of all organs [1]. In the literature, different types of tissue-resident mesenchymal stromal cells (MSCs) are described however, it is not clear if these cells are specific only for tissue regeneration from which they originate or whether their heterogeneity allow them to differentiate into various types of cells. MSCs isolated from various tissues share a number of nonhematopoietic cell markers including CD29, CD44, CD73, CD90, CD105, and MHC class I antigens. Nonimmunogenic properties of MSC are permitted by the lack of MHC class II antigens and lack of costimulatory molecules CD40, CD80, and CD86. These characteristics make MSCs promising candidates for new therapeutic strategies in transplantation and regenerative medicine.

Cells bearing MSC characteristics have been isolated from different organs and tissues of the human body including BM, adipose tissue, skin, muscle, tendon, bone, brain, liver, kidneys, lungs, spleen pancreas thymus, synovial membrane, and umbilical cord [2]. Intensive studies on MSCs are performed from years however, the location and role of native MSCs within their own tissue environment in vivo are not fully explained, mainly because of the lack of specific markers allowing their precise recognition [3]. In self-renewing organs, stromal cells reside in specific niches that constitute the microenvironment in which tissue-specific progenitor cells are maintained in a quiescent state. After activation signal delivery, progenitor cells proliferate and migrate to the sites of injury where they differentiate and acquire the mature phenotype [4]. Tissue-specific progenitor cells niche homeostasis is regulated by the division of progenitor cells, which maintain the quantity of primitive and committed cells within the tissue [5].

MSC originated from different tissue locations exhibited many common characteristics however, some markers are distinguishing for differentiation potential of these cells. This review is introducing the similarities and differences between MSCs originated from different type of tissues based on their surface markers and their regenerative potential in organs where they reside and their multipotential ability to differentiate into other lineages.

2. Mesenchymal Stem Cell of Bone Marrow Origin

Up to date, MSCs originated from adult bone marrow stroma are the best characterized mesoderm-derived stromal cells with multipotent differentiation capacity. The term of MSC was introduced by Caplan in 1991 as a type of adult stem cells with natural potential to differentiate into diverse mesenchymal cell types including osteoblasts, chondrocytes, adipocytes and others [6]. Historically, MSCs were isolated for the first time from the bone marrow by Friedenstein as a fibroblastic precursors with unknown anatomical location in the BM environment [7]. These cells were characterized by plastic adherent capacity with fibroblast-like morphology, extensive proliferation ability, and clonal expansion as confirmed by colony-forming unit fibroblast assay (CFU-F). Moreover, heterotopic transplantation of BM cells into different immunoprivileged site, including renal capsule, resulted in ectopic bone formation suggesting that osteogenic precursors are present within BM environment.

Since that time, extensive research on MSCs of bone marrow origin was performed to characterize biology and surface epitopes of MSCs. MSCs are heterogenic populations and express variety of surface epitopes including integrin receptors (CD29, CD49α), cell adhesion molecules (CD44, CD54, CD58, CD62L, CD105, CD106, CD146, and CD166), enzymes (CD39, CD73), growth factor receptors (CD140b, CD271, CD340, and CD349), intermediate filaments (nestin, vimentin, desmin, and neurofilament), and embryonic antigens (SSEA-1), but none of these molecules are specific for BM-derived MSCs (Table 1) [2, 8]. Isolation of MSCs based on STRO-1 [9], antinerve growth factor receptor CD271 [10, 11], or cell adhesion molecule CD146 expression [12, 13] documented their heterogeneity and clonogenic capacity of these cells. However, further studies documented that MSCs isolated based on CD271 and CD146 surface markers constitute two distinct populations of MSCs of BM origin and these subtypes may have different function during development and aging [14].

Heterogeneity of MSCs, different isolation procedure of native stromal cells, and diverse culture conditions were a reason for defining by Mesencymal and Tissue Stem Cell Committee of the International Society for Cellular Therapy minimal criteria which characterize human mesenchymal stem cells as (i) plastic adherent cells, (ii) with expression of CD73, CD90, and CD105 surface markers and lack of expression of hematopoietic markers CD34−, CD45−, CD14−, CD79α−, and HLA-DR−, and (iii) multilineage differentiation potential into osteoblasts, adipocytes, and chondroblasts [15]. If the above criteria are not completed, the term “mesenchymal stem cells” should be used for bone marrow-derived adherent cells or other MSC-like cells of different origin.

Extensive research describing MSC phenotype and biology has been performed on human BM-derived MSC in vitro, but there is still a little evidence on their phenotype in their natural in vivo environment. Recent studies on trabecular bone biopsy specimens documented the presence of cells with pattern of MSC antigen expression with different morphology and microanatomic localization [8]. Nonreticular stromal cells including round stromal cells and bone lining cells express CD73, CD140b, and CD271 antigens. Round stromal cells additionally express CD10, whereas bone lining cells are distinguished by neural ganglioside (GD2) expression. Reticular stromal cells such as fibroblastic reticular cells and adipose stromal cells (ASC) are overlapping CD10 and CD146 antigens and are distinguished by the presence of GD2 (on fibroblastic reticular cells) and CD73 (on ASC) [8]. In many studies, topography of MSCs in the BM environment is introduced as the cell lining the outer surfaces of blood vessels and perivascular cells and these cells express CD146 antigen [8, 16, 17]. MSCs sorted based on STRO-1+CD146+ phenotype expressed smooth muscle actin alpha (αSMA) which is also specific for pericytes [18]. Tormin studies introduced that CD146+/CD271+ BM cell fraction comprises both sinusoidal perivascular cells and cells residing in the BM environment, whereas bone lining MSC expressed CD271 alone [19]. All these observations suggested that MSCs residing in the medullary cavity, endosteum, and BM stroma represent distinct fractions of MSCs contributing to different progenitors development at the natural BM microenvironment.

In the BM environment, MSCs are involved in tissue homeostasis by contributing to hematopoietic stroma formation and regulatory molecules production including stem cell factor (SCF) and chemokine CXCL12, factors necessary for hematopoietic stem cell (HSC) niche regulation and maintenance. Downregulation of CXCL12 expression in reticular cells and osteoblasts results in HSC mobilization to the periphery and loss of B-cell progenitors, whereas the deletion of Cxcl12 from stromal cells in perivascular region has influence on long-term HSC repopulating activity and common lymphoid progenitors [20]. However, perivascular HSC niche is more complex and is supported by other cell types including vessel endothelial cells, sympathetic nerves, nonmyelinating Schwann cells, macrophages, and osteoblasts, which in cooperation with perivascular MSC are responsible for self-renewal, proliferation, and trafficking of HSC, thus maintaining the pool of HSC [20]. Therefore, the level of CXCL12 expression on MSC originated from different HSC niches confirmed MSC diversity in the BM compartment and their influence on HSC and lymphoid progenitors activity.

Recent studies documented that stromal cell originated from different tissues (other than BM) showed significant differences in their differentiation and molecular phenotype and these findings suggest that stromal cells from other sources may be not able to substitute stromal cells of bone marrow origin [21].

3. The Liver Progenitor Cells

Regenerative potential of the liver is accomplished by resident hepatocytes and cholangiocytes when moderate liver injury occurs. However, self-renewal capacity of hepatocytes is limited when massive liver damage or partial hepatectomy takes place. Under these certain conditions, liver stromal/progenitor cells in humans [22, 23] and oval cells in rodents [24, 25], named for their morphological appearance as small cells with oval nuclei, can participate in liver regeneration. Human hepatic progenitor cells are bipotent precursors of hepatoblasts and cholangioblasts and reside at ductal plates in fetal liver and in canals of Hering in the vicinity of the portal triads of acini in adult livers [23]. They express specific marker EpCAM (epithelial cell adhesion molecule) allowing for their immunoselection (Table 1). EpCAM positive cells characterize high clonogenic activity for above 150 population doubling. Moreover, pluripotency of EpCAM positive cells and the ability to differentiate into biliary progenitors and hepatoblasts permitted self-renewal capacity of these cells. Except EpCAM, hepatic progenitor cells express CD29, CD133, and NCAM (CD56) molecules, and they are negative for hematopoietic markers (CD34, CD45, CD38, and CD14), for endothelial cell markers (VEGFR, vWF, and CD31), and for mesenchymal markers defined by authors as CD146, desmin, and α-smooth muscle actin. However, ex vivo clonogenic expansion of EpCAM positive cells revealed the presence of mesenchymal “companion” cells, which penetrate the colonies and were found throughout them. The mesenchymal “companion” cells represent two distinct populations: angioblasts positive for VEGFR, vWF, CD31, and CD117 (c-kit) and hepatic stellate cells that expressed CD146+, desmin, and α-smooth muscle actin. Additional rigorous immunoselection for EpCAM+ cells proved that paracrine signaling from mesenchymal “companion” cells is essential for EpCAM+ cells survival [23]. Presumably, among mesenchymal “companion” cells, pericytes (CD146+, CD90+, and CD140b+), normally localized around periportal blood vessels in human fetal and adult liver, contribute to clonogenic potential of EpCAM cells [26]. Studies on rodent model introduced that EpCAM is expressed on oval cells and on cholangiocytes, while TROP2 associated protein, a member of EpCAM family, is expressed exclusively in oval cells, indicating that TROP2 is a valuable marker for oval cells characteristics. TROP2 expression, upregulated in oval cells in injured liver, increases the possibility to modulate and/or augment the intracellular signaling of EpCAM to support proliferation and migration of oval cells into liver parenchyma [27].

Oval cells, recognized as facultative progenitor cells in adult liver that normally reside in the portal area of the liver, are proliferative quiescent. After severe injury of the liver, oval cells become activated and migrate into liver parenchyma and differentiate into hepatocytes and cholangiocytes. However, the origin of oval cells is controversial, and studies documented that oval cells are of bone marrow origin [28]. In severe liver injury, hepatocytes upregulate expression of SDF-1α, a potent chemoattractant for hematopoietic cells CXCR4+. Oval cells express CXCR4, the only receptor for SDF-1α. Interaction of SDF-1α/CXCR4 is essential to initiate activation of oval cells, when hepatocyte proliferation is impaired, and maintain stem cell niches through the control of progenitor cell migration by possible recruitment of a second wave of bone marrow origin progenitor cells to the injured side of the liver [25, 29].

The hepatic stellate cell represents the fraction of liver-resident cells with star-like morphology, located between liver sinusoidal endothelial cells and hepatocytes. Perisinusoidal stellate cells represent MSC of the liver and regulate essential hepatic physiological and pathological processes. During normal conditions, stellate cells are quiescent and have low proliferation rate, but, after liver injury, these cells progressively activate and change their dormant phenotype for active myofibroblastic-like phenotype. Myofibroblastic-like phenotype is characterized by the expression of α-smooth muscle actin (α-SMA) and desmin intermediate filaments. Moreover, activated stellate cells express neural markers including glial fibrillary acidic protein (GFAP), nestin, and N-CAM. These observations indicate a possibility of neural origin of liver stellate cells. These cells express also CD271, known as p75NTRF (nerve growth factor receptor family), which is a marker for mesenchymal stromal cells and is used for their positive isolation. However, the cellular phenotype of primary hepatic stellate cells depends on their fetal or adult liver origin and is highly dynamic, time dependent, and culture conditions dependent. At early stage, culture fetal CD271 positive cells did not express α-SMA and CD90, but after longer cultivation these cultured CD271 cells exhibit strong expression of these markers. In contrast, freshly isolated CD271 cells from adult liver expressed all the markers of stellate cells [30]. However, both types of CD271 cells expressed phenotype characteristic for MSCs including CD73 and CD105 and were negative for hematopoietic markers CD34 and CD45. Our own studies on tissue-resident stromal cells documented tissue distribution of cells with self-renewal capacity in the liver, expressing CD73, CD90, and c-kit, and these cells are localized in the periportal area of the liver as illustrated in Figure 1 [31].

Thus, regenerative capability of human liver is not associated with one type of liver progenitor cells with regenerative potential. Rather cooperation between different types of stem cells of the liver is necessary to maintain hepatic cells integrity and homeostasis.

4. Skeletal Muscle Mesenchymal Progenitor Cell

Skeletal muscle, similar to the most of postnatal tissues, contains naturally occurring pool of resident adult progenitor cells maintaining regenerative potential of skeletal muscle. The principal progenitor cells responsible for muscle regeneration are satellite cells, a quiescent bipotent tissue-specific cell population located between the basal lamina and sarcolemma [32]. Activation of satellite cells is triggered by muscle injury and is controlled by proximal signals from muscle niche, microvasculature, and inflammatory cells [33], as well as systemic factors [34]. Activated satellite cells act as stromal/progenitor cells contributing to the repair of damaged myofibers, or they are able to generate new myofibers following cell division and fusion with each other or with the existing myocytes. Moreover, satellite cells have the ability to replenish a reserve pool of tissue-resident progenitor cells in skeletal muscle via self-renewal capacity [35]. Quiescent satellite cells express CD34, CD56, and Myf5 surface antigens and paired box transcription factor Pax7 however, expression of CD34+ declined during differentiation into myoblasts [36]. Our own studies proved that MSC markers, CD73 and CD90, were expressed on single stem cells of examined skeletal muscle and were localized in the specific tissue compartments between the basal lamina and sarcolemma of myofibers of the muscle [31]. Moreover, skeletal muscle progenitor cells, but not progenitor cells present in the skin, liver, or heart exclusively express transcriptional factor Pax7 (Figure 1).

Satellite cell pool is relatively stable during the life however, it may differ in specific muscle. It has been suggested that satellite cells consist of two distinct populations, one responsible for muscle regeneration, but their number is decreased with age, and the second which is activated in response to severe muscle injury and remains at constant amount throughout life [1, 37].

In addition to satellite cells, a variety of tissue-resident progenitors existing in skeletal muscle plays important role in the maintenance of tissue homeostasis [32]. Myogenic potential of nonsatellite progenitor cells was identified in a cell population residing in the muscle interstitium in the neonate [38]. These cells demonstrate multilineage potential and belong to mesenchymal progenitor/stromal cells (MSCs) as confirmed by broad range of gene expression common to MSC [39]. These muscle progenitor cells are characterize by the expression of CD34, stress mediator PW1, but they are negative for Pax7 (PW1+/Pax7− interstitial cells, PICs). Studies showed thatthese cells contribute to new myofibers formation and satellite cells generation as documented in vitro when cocultured with myoblasts or in vivo when transplanted into regenerating muscle environment. However, PW1+/Pax7− populations are negative for endothelial markers as proved by CD31 negative staining [38].

Another muscle-resident population of nonsatellite progenitor cells is bipotent fibro/adipogenic progenitors (FAPs) localized in the muscle interstitium and neighboring to muscle-associated blood vessels. These cells are phenotypically CD31−/CD45− and strongly express PDGFα and vimentin, markers associated with mesenchymal progenitors [40]. The majority of FABs (over 90%) have adipogenic capacity. However, these cells differ from PICs as they do not demonstrate direct myogenic potential. Mesenchymal FAPs progenitors, but not PW1+ cells, contribute to muscle regeneration by paracrine factors secretion of IL-6, IGF-1, and Wnt1 which markedly augmented myoblasts to terminal differentiation [41, 42].

Myogenic potential was also confirmed in endothelial-like mesodermal progenitors with pericytic features [43]. Pericytes, located within the basement membrane of vessels, in the human skeletal muscle represents myogenic precursors distinct from satellite cells. Muscle-resident pericytes are negative for myogenic markers including Myf5, MyoD, and MyoG. They are identified by alkaline phosphatase expression (AP) and they express neuroglial 2 proteoglycan (NG-2), platelet-derived growth factor receptor β (PDGFRβ), and smooth muscle actin alpha (αSMA). Pericytes from the muscle stimulated in vitro are capable of myogenic differentiation. In vivo studies, on mouse muscular dystrophy, documented that pericyte transplanted into scid-mdx mice colonize host muscle and generate muscle fibres expressing human dystrophin [43]. Subsequent studies demonstrated that proportion of pericytes are capable to fuse with myofibers during early postnatal period and contribute to myogenesis.

Muscle-resident mesenchymal stromal/progenitor cells constitute heterogenous population of cells with diverse differentiating capability and play important role in tissue homeostasis. Most of them, like satellite cells, PICs, and pericytes, have direct myogenic differentiation capacity in vivo, whereas mesenchymal progenitors FAB/MSC effectively support myogenesis by paracrine growth factors secretion. Thus, effective regenerative potential of damaged skeletal muscle is associated with collaborative interactions between multiple heterogenous muscle progenitor cell types residing in the tissue.

5. The Skin-Derived Multipotent Stromal Cells

The presence of cells with regenerative potential in the skin can be attributed to maintain skin homeostasis and response to damage. Skin consists of epidermis and dermis layers, which are under steady regeneration process and contain a number of cells originating from mesoderm and ectoderm [44, 45]. Self-renewal capacity of the epidermis and hair follicles is dependent on precursor cells that exist in the epidermis, the dermal papillae, and the bulge. The presence of progenitor-like cells or MSCs in the skin was confirmed by the identification of several types of adult skin stromal or progenitor cells localized in both layers of the skin including dermal stromal cells and epidermal stromal cells [20, 45, 46]. Moreover, skin-derived precursors localized in several other skin structures such as hair follicles, blood vessels, sensory receptors, and nerve endings contribute to regeneration process and maintenance of the skin integrity. Isolated endogenous skin-derived precursors have the ability to proliferate for many passages with unspecialized phenotype, but under specific conditions they are able to differentiate into specific cell types including a neuroectodermal and mesodermal lineages. In the skin are also present different type of MSC, and their biological properties are different in cell culture. Adherent skin-origin MSCs are growing in the presence of serum, express markers specific for mesenchymal stem cell lineages CD73, CD90, and CD105, are negative for hematopoietic merkers including CD34, CD45, CD14, CD31, and HLA-DR, and are negative for nestin and positive for fibronectin, vimentin, and collagen type I. In contrast, skin-derived precursors in culture without serum form floating spheres and express nestin, the marker distinguishing them from plastic adherent cells [20, 45, 47]. Moreover, serum-free expanded floating spheres represent skin-derived precursors with limited mesodermal but higher neurogenic differentiation potential comparable to neural crest stem cells [45].

Diversity of human MSC of dermis origin was also confirmed in studies on mesenchymal progenitors isolated from foreskin samples [48]. In situ analysis performed on skin samples revealed that MSC markers CD73, CD90, and CD105, as well as CD271 and SSEA-4, are expressed on different dermal cell types including endothelial cells (CD31+, CD34+) and leukocytes (CD45+). However, CD73, CD90, and CD105 positive cells lacking endothelial and leukocyte markers were also identified and these cells were characterized as a potential mesenchymal progenitor cells. Isolated dermal mesenchymal progenitors expressed surface markers similar to bone marrow-derived MSC. Dermal stromal cells represent very heterogeneous population, and except mesenchymal progenitors, within dermal plastic-adherent population, differentiated fibroblasts are present. Immunoselection of MSC based on CD271+ and SSEA-4 markers from adherent dermal cells confirmed their mesenchymal differentiation capacity and thus distinguished dermal MSC from differentiated fibroblasts. However, CD271+ cell population revealed higher adipogenic, osteogenic, and chondrogenic differentiation capacity compared to SSEA+ cells, which represent cell population of mesenchymal origin with differentiation potential limited to adipogenesis [48].

In the skin, taken from human thigh, we identified markers associated with phenotype of tissue-specific stromal cells, localized in the basal layer of epidermis and in the epithelium of adnexal structure of the skin (c-kit, CD90). CD73 positive cells were rather present in the perivascular area (Figure 1). These observations again proved diversity of tissue-resident stromal cells associated with their specific niche.

Thus, the skin, especially the foreskin and skin removed during aesthetic surgery, constitutes a selected biological waste material and can serve as an alternative source of progenitor-like cells for these MSCs of bone marrow origin, which may be applied for studies on tissue repair and cell-based therapy in regenerative medicine.

6. Cardiac Stem Cells

Human heart contains a population of primitive cells with self-renewal, clonogenic, and multipotent properties and these cells are able to differentiate into cardiomyocytes and coronary vessels. Resident cardiac progenitor cells represent heterogeneous population classified according to their biologic properties and surface markers for side population (SP), c-kit+ (CD117+), stem cell antigen-1 (Sca-1+), Islet 1+, SSEA-1+, and “cardiospheres” [49]. In the human myocardium, cardiac progenitor cells are localized within the cardiac niches composed of myocytes and fibroblasts, which represent the supporting cells, permitting maintenance of the balance between cardiac stem cell quiescence and activation [5]. Cardiac progenitor cells, with phenotype of CD73+, CD90+, and c-kit+, connected to myocytes and fibroblasts in the cardiac niches, were identified in our studies on tissue distribution of stromal/progenitor cells (Figure 1) [31].

The side population cardiac progenitor cells are heterogeneous and represent different subpopulations identified by expression of VE-cadherin, CD31, CD34, and Sca-1 and consist of vascular endothelial cells, smooth muscle cells, and mesenchymal progenitors including cardiomyogenic precursors. In rodents, SP cardiac progenitors were described as Sca-1+, c-kit+, CD34+, CD31−, and CD45− cells expressing cardiac specific transcriptional factor. After isolation and in vitro culture, SP cardiac progenitor cells acquired a cardiomyocyte phenotype documented by expression of sarcomeric proteins, troponin and α-cardiac actinin [49, 50]. Upon in vitro stimulation, these cells showed multipotent ability to differentiate not only into cardiomyocytes but also into typical neural crest-derived lineages including neurons, glia, and smooth muscle [51]. In vivo studies on the rat model, documented the ability of SP cardiac progenitor cells to home damaged myocardium and to differentiate into cardiomyocytes and endothelial cells after intravenous infusion [52].

C-kit is a tyrosine kinase receptor for the stem cell factor primarily described on the hematopoietic stem cells of bone marrow origin [53]. A distinct resident cardiac stem cell population supporting cardiac regeneration, positive for c-kit, and negative for blood lineage markers CD34−, Lin−, and CD45− was reported for the first time by Beltrami et al. [54]. Subsequent studies confirmed the potential of c-kit positive cardiac progenitor cells in reducing infarct size and improving cardiac function after myocardial infarction [55]. Isolation and in vitro expansion of c-kit positive cells from cardiac tissue revealed differentiation potential to cardiomyocytes as confirmed by the expression of cardiomyocyte markers including α-cardiac actinin, cardiac myosin, desmin, and connexin [54, 55]. However, as reported by Tallini et al., c-kit positive cells act as cardiac progenitors until the neonatal phase, but in the adult myocardium they are rather responsible for neoangiogenesis [56]. C-kit+CD45− cells isolated from human cardiac biopsies coexpress endothelial progenitor cell markers CD31, CD34, CXCR4, and FLK-1, indicating further differentiation into endothelial cells [57]. Recent observations introduced the theory that c-kit positive cells constitute two populations, where the high c-kit+ cells work as cardiac progenitors and the low c-kit+ population might function as MSC [58]. Pluripotency of c-kit positive cells was confirmed by the differentiation ability into adipocytes and skeletal muscle myocytes.

Hypoxia favors cardiac progenitor cell quiescence, while normoxia is necessary for their activation and balance between hypoxic and normoxic cardiac progenitor cells may be present in young heart, whereas defects in tissue oxygenation occurring in the old myocardium may disrupt homeostatic control. Very recent studies reported that in senescent myocardium an increased number of quiescent c-kit positive cardiac progenitor cells with intact telomeres that cannot reenter the cell cycle are present, whereas myocyte repair is controlled by dividing cardiac progenitor cells with shortened telomeres. This observation suggests that a pool of functionally competent cardiac progenitor cells, nested in hypoxic niches in the senescent myocardium, can promote myocyte regeneration after activation by stem cell factor [59].

Sca-1 positive cells within myocardium represent heterogeneous subpopulation of cardiac progenitor cells based on the different subset of coexpressed stem markers. Cardiac progenitor cells expressing Sca-1+CD31+ and lacking the blood cell lineage markers c-kit, FLT-1, CD45, and CD34 negative were identified in adult murine myocardium [60]. These cells can differentiate into cardiomyocytes with the expression of structural cardiac genes. Sca-1 positive cells stimulated with oxytocin expressing c-kit, CD45, and CD34 generated beating cardiomyocytes, whereas Sca-1+CD45− cells in the same conditions revealed multipotent differentiation capacity into osteogenic and adipogenic lineages [61].

Islet-1 positive cells are considered as true cardiomyocyte progenitors appearing during embryogenesis and contribute to the right ventricle and outflow tract, although, it is unclear whether these cells exist in adult myocardium [62]. Within myocardium, cardiac progenitor cells expressing stage-specific embryonic antigen-1 (SSEA-1) are present. These cells represent a population of an immature pool of embryonic progenitors that differentiate into myocardial and endocardial cells at the neonatal stage of heart development. It has been suggested that SSEA-1+ cardiac stem cells can give rise to more committed cardiac progenitors expressing c-kit and Sca-1 [63].

Resident cardiac progenitor cells are abundantly present within the myocardium in niches preferentially located in the atria and apex and in the ventricle and effectively preserve the integrity of the tissue in the physiological conditions. However, the number of resident cardiac progenitor cells might be insufficient to repopulate injured tissue after extensive myocardial infarction. This may suggest that inherent ability of the myocardium to regenerate damaged myocytes after myocardial infarction is insufficient. This may be explained by the action of detrimental factors such as (i) deprived oxygen delivery in the infarct area leading not only to the cardiomyocytes necrosis but also to the death of resident progenitor cells within the infarct site, (ii) and resident cardiac progenitor cells, which accumulate acutely in the border of the infarct and cannot migrate from the viable tissue to the injured site because their translocation to the damaged myocardium is hampered. This is associated not only with anatomical barrier (scar formation) but also with limited production of growth factors (hepatocyte growth factor, insulin growth factor, and stroma-derived growth factor) facilitating recruitment of cardiac progenitor cells to the site of injury, and with inflammatory milieu of the injured myocardium which may have a negative effect on cardiac progenitor cells viability and differentiation [64, 65].

Thus, autologous resident cardiac progenitor cells, isolated from the adult myocardium, may offer distinct advantages over other adult stem cells for the therapy of cardiovascular diseases as they are tissue-specific and precommitted to the cardiovascular lineages.

7. The Lung Stromal and Progenitor Cells

The lung is a conditionally renewing organ and turnover of airway epithelial cells is less than 1% per day in the steady state conditions, and this regenerative capacity of the lung is in contrast to the continuously renewing tissue, such as bone marrow, with the ability to generate approximately 10 9 hematopoietic cells daily. However, following severe injury, self-renewing potential of stromal and epithelial progenitor cells of the lung increases rapidly and compensatory growth of multipotent cells warrants proper regeneration of the lung [66]. Within the lung many diverse epithelial cell types exist and they are distributed in several different regional microenvironments along the pulmonary tract. Many studies on mouse models and a smaller number of literature reports on human lungs describe presumed populations of adult endogenous airway and alveolar epithelial progenitor cells however, characterization and classification of these cells into a hierarchy are still controversial [67].

The organization of endogenous stromal and epithelial progenitor cells in the adult lung is specific for their regional distribution and function along the proximal-distal axis of the airway tree. The proximal part of the airway comprises the cartilaginous trachea, lined by columnar pseudostratified epithelial cells with submucosal glands, and includes basal, secretory, ciliated, and neuroendocrine cells. Basal cells represent progenitor/stromal cells of bronchiolar epithelium and are characterized by the expression of nerve growth factor receptor (NGFR), p63, cytokeratin-5, cytokeratin-14, and aquaporin 3. After isolation and ex vivo culture, they formed clonal structures positive for ciliated and club cells (known as Clara cells) [68, 69]. A population of basal cells can migrate from the bronchiolar niche into damaged alveolar epithelium and proliferate to repair alveolar region [69].

The distal part of the airway is lined with columnar epithelial cells and comprises different population of cells including club cells, ciliated cells, goblet cells, and neuroendocrine cells [66]. During epithelial homeostasis, club cells can self-renew and generate ciliated cells, whereas ciliated cells do not have the ability for self-regeneration [70, 71]. Within the club cells, residing along the distal axis of the airway tree, a distinct population of cells known as variant club cells is present and they are located at the bronchoalveolar duct junction. The variant club cells with self-renewal potential and differentiation capacity into club cells are able to repair bronchiolar epithelial cells after naphthalene injury [71]. Another population of distal airway stromal and progenitor cells is rare population of cells called bronchioalveolar stem/progenitor cells [66]. Bronchioalveolar progenitor cells are positive for the stem cell marker Sca-1, positive for EpCAM, and negative for hematopoietic (CD34, CD45) and endothelial cell markers (CD31) [72]. In vitro studies documented that bronchioalveolar progenitor cells are able to differentiate into bronchiolar and alveolar colonies and have self-renewal capability. Moreover, their number increases after bronchiolar injury, and this suggests their role in tissue regeneration [73].

Terminal part of the lung constitutes alveoli with specific alveolar progenitor cells, which differentiate into surfactant-producing alveolar type II cells and gas-exchanging alveolar type I cells [71]. A population of alveolar progenitor cells, expressing laminin receptor α6β4 integrin, is located in the alveolar epithelium and is capable to contribute to airway and alveolar tissues regeneration in experimental model after parenchymal injury [74].

Resident lung mesenchymal stromal cells constitute a key element of epithelial progenitor niches along the proximal-distal axis of the airway tree [71, 72, 75]. The lung mesenchymal stromal cells secrete FGF 10, a critical factor necessary for directing differentiation in the developing lung [71]. Moreover, it has been documented that lung mesenchymal stromal cells, EpCAM negative and Sca-1 positive, cocultured with lung epithelial progenitor cells (EpCAM positive), support their proliferation and differentiation and generate colonies including airway, alveolar, or mixed lung epithelial cell lineages [75].

Regional stromal and progenitor cells such as submucosal gland/duct progenitor cells, basal cells, variant club cells, bronchioalveolar stem/progenitor cells, and alveolar progenitor cells that reside in distinct niches of the respiratory tract are responsible for the maintenance of specific epithelial cell lineages integrity in the specific region of the airways. Different populations of tissue-resident stromal and progenitor cells are involved in region-specific homeostasis and tissue repair after the injury of the lung. Thus, homeostasis of the lung is a highly coordinated process of proliferation and differentiation of lung stromal and progenitor cells and requires a balance between immune regulation and promotion of tissue regeneration.

8. Summary

Multipotent MSCs reside in specific tissue niches composed of cells creating specific microenvironment for tissue-resident progenitor cells and facilitate them to maintain tissue homeostasis. Niche cells provide signals which regulate and control the balance of self-renewal and differentiation capacity of stem/progenitor cells residing in them. The niche also controls stem/progenitor cell division and activity to preserve cancer formation. The balance of progenitor cell quiescence and activity is a hallmark of a functional niche and is regulated by internal (e.g., DNA damage) and external signals leading to self-renewal and differentiation of progenitor cells.

MSC can be easily isolated from various tissue sources, expanded in the culture, and appropriately differentiated under proper conditions. Depending on their tissue of origin, MSCs are predisposed to give rise to the type of tissue cells from where they are coming. Thus, MSCs from adult human tissues are ideal candidates for tissue regeneration and tissue engineering. However, MSCs do not only contribute to structurally tissue repair, but MSCs possess potent immunomodulatory and anti-inflammatory effects, and through direct cell-cell interaction or secretion of various bioactive factors they may have an effect on local tissue repair by modulation of local environment.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgment

This work is supported by the National Science Center Grant N N407 121940.

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Copyright

Copyright © 2016 Aleksandra Klimczak and Urszula Kozlowska. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Connective Tissue Lab

In contrast to epithelia, connective tissue is sparsely populated by cells and contains an extensive extracellular matrix consisting of protein fibers, glycoproteins, and proteoglycans. The function of this type of tissue is to provide structural and mechanical support for other tissues, and to mediate the exchange of nutrients and waste between the circulation and other tissues. These tissues have two principal components, an extracellular matrix and a variety of support cells. These two components will be the focus of this lab.

Most frequently, the different types of connective tissues are specified by their content of three distinguishing types of extracellular fibers: collagenous fibers, elastic fibers, and reticular fibers.

Collagen Fibers

Collagenous fibers consist of types I, II, or III collagen and are present in all types of connective tissue. Collagenous connective tissue is divided into two types, based upon the ratio of collagen fibers to ground substance. Ground substance is an aqueous gel of glycoproteins and proteoglycans that occupies the space between cellular and fibrillar elements of the connective tissue.

  • Loose (areolar connective tissue) is the most abundant form of collagenous connective tissue. It occurs in small, elongated bundles separated by regions that contain ground substance.
  • Dense connective tissue is enriched in collagen fibers with little ground substance. If the closely packed bundles of fibers are located in one direction, it is called regular if oriented in multiple directions, it is referred to as irregular. An example of regular dense connective tissue is that of tendons an example of irregular dense connective tissue is that of the dermis.

Reticular Fibers

Reticular fibers are composed of type III collagen. Unlike the thick and coarse collagenous fibers, reticular fibers form a thin reticular network. Such networks are widespread among different tissues and form supporting frameworks in the liver, lymphoid organs, capillary endothelia, and muscle fibers.

Elastic Fibers

Elastic fibers contain the protein elastin, which co-polymerizes with the protein fibrillin. These fibers are often organized into lamellar sheets, as in the walls of arteries. Dense, regular, elastic tissue characterizes ligaments. Elastic fibers are stretchable because they are normally disorganized – stretching these fibers makes them take on an organized structure.

Cells of Connective Tissue

Fibroblast

Fibroblasts are by far the most common native cell type of connective tissue. The fibroblast synthesizes the collagen and ground substance of the extracellular matrix. These cells make a large amount of protein that they secrete to build the connective tissue layer. Some fibroblasts have a contractile function these are called myofibroblasts.

Macrophage

The macrophage is the connective tissue representative of the reticuloendothelial, or mononuclear phagocyte, system. This system consists of a number of tissue-specific, mobile, phagocytic cells that descend from monocytes - these include the Kupffer cells of the liver, the alveolar macrophages of the lung, the microglia of the central nervous system, and the reticular cells of the spleen. You will encounter each of these later in the course for now, make sure you recognize that they all descend from monocytes, and that the macrophage is the connective tissue version. Macrophages are indistinguishable from fibroblasts, but can be recognized when they internalize large amounts of visible tracer substances like dyes or carbon particles. Macrophages phagocytose foreign material in the connective tissue layer and also play an important role as antigen presenting cells, a function that you will learn more about in Immunobiology.

Mast Cell

Mast cells are granulated cells typically found in connective tissue. These cells mediate immune responses to foreign particles. In particular, they release large amounts of histamine and enzymes in response to antigen recognition. This degranulation process is protective when foreign organisms invade the body, but is also the cause of many allergic reactions.

White Fat Cell

White fat cells or adipocytes are specialized for the storage of triglyceride, and occur singly or in small groups scattered throughout the loose connective tissue. They are especially common along smaller blood vessels. When fat cells have accumulated in such abundance that they crowd out or replace cellular and fibrous elements, the accumulation is termed adipose tissue. These cells can grow up to 100 microns and usually contain once centrally located vacuole of lipid - the cytoplasm forms a circular ring around this vacuole, and the nucleus is compressed and displaced to the side. The function of white fat is to serve as an energy source and thermal insulator.

Brown Fat Cell

Brown fat cells are highly specialized for temperature regulation. These cells are abundant in newborns and hibernating mammals, but are rare in adults. They have numerous, smaller lipid droplets and a large number of mitochondria, whose cytochromes impart the brown color of the tissue. The electron transport chain of these mitochondria is disrupted by an uncoupling protein, which causes the dissipation of the mitochondrial hydrogen ion gradient without ATP production. This generates heat.

Cartilage

Cartilage is a specialized form of connective tissue produced by differentiated fibroblast-like cells called chondrocytes. It is characterized by a prominent extracellular matrix consisting of various proportions of connective tissue fibers embedded in a gel-like matrix. Chondrocytes are located within lacunae in the matrix that they have built around themselves. Individual lacunae may contain multiple cells deriving from a common progenitor. Lacunae are separated from one another as a result of the secretory activity of the chondrocytes. Three kinds of cartilage are classified according to the abundance of certain fibers and the characteristics of their matrix.

Hyaline Cartilage

Hyaline cartilage has a matrix composed of type II collagen and chondromucoprotein, a copolymer of chondroitin sulfates A and C with protein. Its high concentration of negatively-charged sulfate groups makes it appear intensely basophilic under H&E. This cartilage is found in the nose, tracheal rings, and where the ribs join the sternum.

Fibrocartilage

Fibrocartilage is distinguished by its high content and orderly arrangement of type I collagen fibers. It is typically located in regions where tendons attach to bones, the intervertebral discs, and the pubic symphysis.

Elastic Cartilage

Elastic cartilage is characterized by the presence of abundant elastic fibers and is quite cellular. It is made up of type II collagen and is located in the auricle of the ear and the epiglottis.


Connective Tissue

The human body is composed of just four basic kinds of tissue: nervous, muscular, epithelial, and connective tissue. Connective tissue is the most abundant, widely distributed, and varied type. It includes fibrous tissues, fat, cartilage, bone, bone marrow, and blood. As the name implies, connective tissues often bind other organs together, hold organs in place, cushion them, and fill space.

Connective tissue is distinguished from the other types in that the extracellular material (matrix) usually occupies more space than the cells do, and the cells are relatively far apart. Fat is an exception, having cells in close contact with each other but with large, nonliving, intracellular lipid droplets, fat contains much more nonliving material than living material.

The matrix of connective tissue typically consists of fibers and a featureless ground substance. The most abundant fiber in connective tissues is a tough protein called collagen. Tendons, ligaments, and the white stringy tissue (fascia) seen in some cuts of meat are composed almost entirely of collagen, as is leather, which consists of the connective tissue layer (dermis) of animal skins. Collagen also strengthens bone and cartilage. Elastic and reticular fibers are less abundant connective tissue proteins with a more limited distribution.

The ground substance may be liquid, as in blood gelatinous, as in areolar tissue rubbery, as in cartilage or calcified and stony, as in bone. It consists mainly of water and small dissolved ions and organic molecules, but the gelatinous to rubbery consistency of some tissues results from enormous protein-carbohydrate complexes in the ground substance. The hard consistency of bone results mainly from calcium phosphate salts in the ground substance.

Some of the cells of connective tissue are fibroblasts (which produce collagen fibers and are the only cell type in tendons and ligaments) adipocytes (fat cells) leukocytes (white blood cells, also found outside the

Connective tissue type and characteristics Functions Locations
Areolar (loose) connective tissue. Loose array of random fibers with a wide variety of cell types Nourishes and cushions epithelia, provides arena for immune defense against infection, binds organs together, allows passage for nerves and blood vessels through other tissues Under all epithelia outer coverings of blood vessels, nerves, esophagus, and other organs fascia between muscles pleural and pericardial sacs
Adipose tissue (fat). Large fat-filled adipocytes and scanty extracellular matrix. Stores energy, conserves body heat, cushions and protects many organs, fills space, shapes body Beneath skin around kidneys, heart, and eyes breast abdominal membranes (mesenteries)
Dense irregular connective tissue. Densely spaced, randomly arranged fibers and fibroblasts. Toughness protects organs from injury provides protective capsules around many organs Dermis of skin capsules around liver, spleen, and other organs fibrous sheath around bones
Dense regular connective tissue. Densely spaced, parallel collagen fibers and fibroblasts. Binds bones together and attaches muscle to bone transfers force from muscle to bone Tendons and ligaments
Cartilage (gristle). Widely spaced cells in small cavities (lacunae) rubbery matrix. Eases joint movements resists compression at joints holds airway open shapes outer ear moves vocal cords forerunner of fetal skeleton growth zone of children's bones External ear, larynx, rings around trachea, joint surfaces and growth zones of bones, between ribs and sternum, intervertebral discs
Bone (osseous tissue). Widely spaced cells in lacunae much of matrix in concentric onionlike layers hard mineralized matrix. Physically supports body, provides movement, encloses and protects soft organs, stores and releases calcium and phosphorus   Skeleton
Blood. Erythrocytes, leukocytes, and platelets in Transports nutrients, gases, wastes, hormones, Circulates in cardiovascular system

bloodstream in fibrous connective tissues) macrophages (large phagocytic cells descended from certain leukocytes) erythrocytes (red blood cells, found only in the blood and bone marrow) chondrocytes (cartilage cells) and osteocytes (bone cells).

The table above lists representative locations and functions of the major types of connective tissue. Further details on connective tissue can be found in textbooks of histology and human anatomy.


NEW FEATURES

Statistics of the newly included TSS data

In this update, we have included a total of 330 533 354 new 36-bp-single-end-read TSS tags. These tags were collected from a series of oligp-capped libraries constructed from eight kinds of human normal tissues (brain, kidney, heart, fetal brain, fetal kidney, fetal heart, fetal thymus and fetal liver) and six cultured cell lines (colon cancer DLD1, B lymphocyte Ramos, bronchial epithelial cells BEAS2B, embryonic kidney HEK293, breast adenocarcinoma MCF7 and fetal lung TIG3 in humans) and fibroblast NIH3T3 cells in mice for details on the origin of the cells, see http://dbtss.hgc.jp/cgi-bin/cell_type.cgi. We constructed the 5′-end libraries using six cell types cultured in different conditions, such as hypoxia or normoxia, and with or without IL4 treatment. Altogether, the current DBTSS includes 31 different cell types or culture conditions, each containing ∼10 million TSS tags ( Table 1). Accession numbers for each dataset are given in http://dbtss.hgc.jp/cgi-bin/accession.cgi. Details of the experimental procedures are also described in http://dbtss.hgc.jp/docs/protocol_solexa.html.

The TSS tags in every dataset were clustered into 500 bp-bins to separate transcription start clusters (TSCs), each of which may represent independent promoters [also see Ref. ( 3) for further details]. 5′-end clusters were further split according to whether they mapped in the vicinity of a RefSeq gene (from −50 Kb upstream from the 5′-end of a RefSeq transcript to the 3′-end of it) or further than 50 Kb away, in what we call an intergenic region. As summarized in Table 1, on average, there were ∼100 000 RefSeq transcription start clusters and 40 000 intergenic ones per cell and per culture condition. In spite of the generally large number of 5′-end clusters consistent with previous observations from ourselves ( 3) and others ( 5), most of the clusters were composed of one or two TSSs. The TSCs having significant expression levels, which may be prioritized for further biological functional characterizations, were relatively rare. The number of TSCs having expression levels of > 5 ppm (part per million tags note that 1 ppm corresponds to 1 copy per cell, assuming every cell contains 1 million mRNA copies) is summarized in Table 1. Detailed statistics on every TSS Seq sub-dataset are shown in http://dbtss.hgc.jp/cgi-bin/cell_type.c. All data can be downloaded on our download site (ftp://ftp.hgc.jp/pub/hgc/db/dbtss/dbtss_ver7/).

Statistics of the new TSS Seq data

Panel A
Sample name Cell type Condition Time course Tag count
DLD1 (Hypoxia with non-tagged RNAi) Fibroblast 1% O2 24 h 7 723 359
DLD1 (Hypoxia with HIF1A RNAi) Fibroblast 1% O2 24 h 7 727 105
DLD1 (Normoxia with HIF1A RNAi) Fibroblast 21% O2 24 h 7 410 902
DLD1 (Hypoxia with HIF2A RNAi) Fibroblast 1% O2 24 h 8 737 554
DLD1 (Normoxia with non-targetedRNAi) Fibroblast 21% O2 24 h 8 644 835
DLD1 (Normoxia with HIF2A RNAi) Fibroblast 21% O2 24 h 8 353 702
Beas2B overexpress STAT6 IL4+ Bcell IL4 4 h 22 954 017
Beas2B overexpress STAT6 IL4− Bcell 21 127 774
Beas2B parent IL4+ Bcell IL4 4 h 15 166 848
Beas2B parent IL4− Bcell 11 628 747
Beas2B stat6 siRNA− IL4+ Bcell IL4 4 h 8 243 100
Beas2B stat6 siRNA− IL4− Bcell 7 857 509
Beas2B stat6 siRNA+ IL4+ Bcell IL4 4 h 5 879 777
Beas2B stat6 siRNA+ IL4− Bcell 5 931 745
Ramos IL4+ Bcell IL4 4 h 15 268 493
Ramos IL4− Bcell 15 759 413
MCF7 O2 1% Breast adenocarcinoma 1% O2 24 h 7 531 326
MCF7 O2 21% Breast adenocarcinoma 21% O2 24 h 13 609 932
TIG O2 1% Fetal lung 1% O2 24 h 8 848 737
TIG O2 21% Fetal lung 21% O2 24 h 9 235 808
293 O2 1% Embryonic kidney 1% O2 24 h 10 590 128
293 O2 21% Embryonic kidney 21% O2 24 h 8 162 101
Fetal Heart Normal fetal tissues 10 182 282
Fetal Kidney Normal fetal tissues 8 424 482
Fetal Liver Normal fetal tissues 4 741 889
Fetal Thymus Normal fetal tissues 7 122 556
Fetal Brain Normal fetal tissues 11 285 710
Brain Normal adult tissues 11 561 960
Heart Normal adult tissues 9 378 901
Kidney Normal adult tissues 11 196 359
Mouse 3T3 Fibroblast 20 246 303
Total 330 533 354
Panel: B
Average 5′-end clusters per cell Average number of TSCs > 5ppm per cell Number of represented genes (coverage against total RefSeq genes)
RefSeq region 105 134 6972 17879/18001 (99%)
Intergenic region 40 537 1083 ND
Panel A
Sample name Cell type Condition Time course Tag count
DLD1 (Hypoxia with non-tagged RNAi) Fibroblast 1% O2 24 h 7 723 359
DLD1 (Hypoxia with HIF1A RNAi) Fibroblast 1% O2 24 h 7 727 105
DLD1 (Normoxia with HIF1A RNAi) Fibroblast 21% O2 24 h 7 410 902
DLD1 (Hypoxia with HIF2A RNAi) Fibroblast 1% O2 24 h 8 737 554
DLD1 (Normoxia with non-targetedRNAi) Fibroblast 21% O2 24 h 8 644 835
DLD1 (Normoxia with HIF2A RNAi) Fibroblast 21% O2 24 h 8 353 702
Beas2B overexpress STAT6 IL4+ Bcell IL4 4 h 22 954 017
Beas2B overexpress STAT6 IL4− Bcell 21 127 774
Beas2B parent IL4+ Bcell IL4 4 h 15 166 848
Beas2B parent IL4− Bcell 11 628 747
Beas2B stat6 siRNA− IL4+ Bcell IL4 4 h 8 243 100
Beas2B stat6 siRNA− IL4− Bcell 7 857 509
Beas2B stat6 siRNA+ IL4+ Bcell IL4 4 h 5 879 777
Beas2B stat6 siRNA+ IL4− Bcell 5 931 745
Ramos IL4+ Bcell IL4 4 h 15 268 493
Ramos IL4− Bcell 15 759 413
MCF7 O2 1% Breast adenocarcinoma 1% O2 24 h 7 531 326
MCF7 O2 21% Breast adenocarcinoma 21% O2 24 h 13 609 932
TIG O2 1% Fetal lung 1% O2 24 h 8 848 737
TIG O2 21% Fetal lung 21% O2 24 h 9 235 808
293 O2 1% Embryonic kidney 1% O2 24 h 10 590 128
293 O2 21% Embryonic kidney 21% O2 24 h 8 162 101
Fetal Heart Normal fetal tissues 10 182 282
Fetal Kidney Normal fetal tissues 8 424 482
Fetal Liver Normal fetal tissues 4 741 889
Fetal Thymus Normal fetal tissues 7 122 556
Fetal Brain Normal fetal tissues 11 285 710
Brain Normal adult tissues 11 561 960
Heart Normal adult tissues 9 378 901
Kidney Normal adult tissues 11 196 359
Mouse 3T3 Fibroblast 20 246 303
Total 330 533 354
Panel: B
Average 5′-end clusters per cell Average number of TSCs > 5ppm per cell Number of represented genes (coverage against total RefSeq genes)
RefSeq region 105 134 6972 17879/18001 (99%)
Intergenic region 40 537 1083 ND

Statistics of the new TSS Seq data

Panel A
Sample name Cell type Condition Time course Tag count
DLD1 (Hypoxia with non-tagged RNAi) Fibroblast 1% O2 24 h 7 723 359
DLD1 (Hypoxia with HIF1A RNAi) Fibroblast 1% O2 24 h 7 727 105
DLD1 (Normoxia with HIF1A RNAi) Fibroblast 21% O2 24 h 7 410 902
DLD1 (Hypoxia with HIF2A RNAi) Fibroblast 1% O2 24 h 8 737 554
DLD1 (Normoxia with non-targetedRNAi) Fibroblast 21% O2 24 h 8 644 835
DLD1 (Normoxia with HIF2A RNAi) Fibroblast 21% O2 24 h 8 353 702
Beas2B overexpress STAT6 IL4+ Bcell IL4 4 h 22 954 017
Beas2B overexpress STAT6 IL4− Bcell 21 127 774
Beas2B parent IL4+ Bcell IL4 4 h 15 166 848
Beas2B parent IL4− Bcell 11 628 747
Beas2B stat6 siRNA− IL4+ Bcell IL4 4 h 8 243 100
Beas2B stat6 siRNA− IL4− Bcell 7 857 509
Beas2B stat6 siRNA+ IL4+ Bcell IL4 4 h 5 879 777
Beas2B stat6 siRNA+ IL4− Bcell 5 931 745
Ramos IL4+ Bcell IL4 4 h 15 268 493
Ramos IL4− Bcell 15 759 413
MCF7 O2 1% Breast adenocarcinoma 1% O2 24 h 7 531 326
MCF7 O2 21% Breast adenocarcinoma 21% O2 24 h 13 609 932
TIG O2 1% Fetal lung 1% O2 24 h 8 848 737
TIG O2 21% Fetal lung 21% O2 24 h 9 235 808
293 O2 1% Embryonic kidney 1% O2 24 h 10 590 128
293 O2 21% Embryonic kidney 21% O2 24 h 8 162 101
Fetal Heart Normal fetal tissues 10 182 282
Fetal Kidney Normal fetal tissues 8 424 482
Fetal Liver Normal fetal tissues 4 741 889
Fetal Thymus Normal fetal tissues 7 122 556
Fetal Brain Normal fetal tissues 11 285 710
Brain Normal adult tissues 11 561 960
Heart Normal adult tissues 9 378 901
Kidney Normal adult tissues 11 196 359
Mouse 3T3 Fibroblast 20 246 303
Total 330 533 354
Panel: B
Average 5′-end clusters per cell Average number of TSCs > 5ppm per cell Number of represented genes (coverage against total RefSeq genes)
RefSeq region 105 134 6972 17879/18001 (99%)
Intergenic region 40 537 1083 ND
Panel A
Sample name Cell type Condition Time course Tag count
DLD1 (Hypoxia with non-tagged RNAi) Fibroblast 1% O2 24 h 7 723 359
DLD1 (Hypoxia with HIF1A RNAi) Fibroblast 1% O2 24 h 7 727 105
DLD1 (Normoxia with HIF1A RNAi) Fibroblast 21% O2 24 h 7 410 902
DLD1 (Hypoxia with HIF2A RNAi) Fibroblast 1% O2 24 h 8 737 554
DLD1 (Normoxia with non-targetedRNAi) Fibroblast 21% O2 24 h 8 644 835
DLD1 (Normoxia with HIF2A RNAi) Fibroblast 21% O2 24 h 8 353 702
Beas2B overexpress STAT6 IL4+ Bcell IL4 4 h 22 954 017
Beas2B overexpress STAT6 IL4− Bcell 21 127 774
Beas2B parent IL4+ Bcell IL4 4 h 15 166 848
Beas2B parent IL4− Bcell 11 628 747
Beas2B stat6 siRNA− IL4+ Bcell IL4 4 h 8 243 100
Beas2B stat6 siRNA− IL4− Bcell 7 857 509
Beas2B stat6 siRNA+ IL4+ Bcell IL4 4 h 5 879 777
Beas2B stat6 siRNA+ IL4− Bcell 5 931 745
Ramos IL4+ Bcell IL4 4 h 15 268 493
Ramos IL4− Bcell 15 759 413
MCF7 O2 1% Breast adenocarcinoma 1% O2 24 h 7 531 326
MCF7 O2 21% Breast adenocarcinoma 21% O2 24 h 13 609 932
TIG O2 1% Fetal lung 1% O2 24 h 8 848 737
TIG O2 21% Fetal lung 21% O2 24 h 9 235 808
293 O2 1% Embryonic kidney 1% O2 24 h 10 590 128
293 O2 21% Embryonic kidney 21% O2 24 h 8 162 101
Fetal Heart Normal fetal tissues 10 182 282
Fetal Kidney Normal fetal tissues 8 424 482
Fetal Liver Normal fetal tissues 4 741 889
Fetal Thymus Normal fetal tissues 7 122 556
Fetal Brain Normal fetal tissues 11 285 710
Brain Normal adult tissues 11 561 960
Heart Normal adult tissues 9 378 901
Kidney Normal adult tissues 11 196 359
Mouse 3T3 Fibroblast 20 246 303
Total 330 533 354
Panel: B
Average 5′-end clusters per cell Average number of TSCs > 5ppm per cell Number of represented genes (coverage against total RefSeq genes)
RefSeq region 105 134 6972 17879/18001 (99%)
Intergenic region 40 537 1083 ND

TSS dynamics viewer

As the expanded DBTSS data contains hundreds of millions of TSS data collected from dozens of different cell types in diverse culture conditions, it is essential to represent the TSS data to meet the users' interests. Otherwise, the database becomes no more than a confusing compilation of massive TSS data. First, we masked the clusters with very low expression levels (<5 ppm at the default setting, although there is an option to show all the TSSs), considering they might be derived from intrinsic transcriptional noise of the cells ( 10) or other experimental errors. Second, we categorized the TSCs in a series of the TSS Seq data so that users can empirically understand the differential usage of the TSSs in different cell types or culture conditions. For graphical representation, we developed a series of new interfaces as shown in Figures 1 and 2. The TSSs corresponding to a particular gene of interest to the user ( Figure 1B) can be retrieved and their differential usage in different cellular circumstances can be represented. Figure 1D exemplifies the tissue-specific alternative promoter. In the zinc finger protein 622 gene (NM_033414), the second upstream alternative promoter (moss green) was selectively used in fetal heart, while a different alternative promoter (light green) is used in the other tissues including adult. Also, using our new search page as shown in Figure 1C, users can search promoters showing significant expression changes in response to particular environmental changes.

Interfaces of the newly implemented ‘TSS tag viewer’. TSS sequence tag information can be retrieved from the top page (A) by following either of the links. The viewers corresponding to each link are represented in the indicated figures. (B) In the ‘Database Search’ form, users can directly specify the 5′-end tags of a gene or a cell type they want to view. (C) In the ‘TSS tag search’ form, users can search TSS tags by specifying cell types, fold induction and/or tag counts. They can also choose which category of tags should be considered (e.g. whether tags of different alternative promoters should be counted separately or not). (D) An example of developmental stage-specific alternative promoters. In the zinc finger protein 622 gene (NM_033414), the promoter indicated in moss green (second panel) is selectively used in fetal heart. The upper and lower panels represent the TSS tag usages in adult and fetal tissues, respectively. Height of the vertical bars represents the number of TSS Seq tags located in the corresponding genomic regions. Different alternative promoters are represented by different colors. Each horizontal line represents the experimental condition from which TSS tags were derived. Legends for the tissues and sum of the TSS tag counts are shown at the right margin. (E) Example of the case in which alternative promoter-specific induction was observed in response to IL-4 stimulation in Ramos cells. In the hypothetical protein LOC746 gene (NM_ 014206), the alternative promoter indicated in red (first panel) is selectively induced while the other alternative promoter indicated in blue (second panel) remained unchanged. The indicated TSS regions are magnified to the nucleotide level in the bottom lower panels.

Interfaces of the newly implemented ‘TSS tag viewer’. TSS sequence tag information can be retrieved from the top page (A) by following either of the links. The viewers corresponding to each link are represented in the indicated figures. (B) In the ‘Database Search’ form, users can directly specify the 5′-end tags of a gene or a cell type they want to view. (C) In the ‘TSS tag search’ form, users can search TSS tags by specifying cell types, fold induction and/or tag counts. They can also choose which category of tags should be considered (e.g. whether tags of different alternative promoters should be counted separately or not). (D) An example of developmental stage-specific alternative promoters. In the zinc finger protein 622 gene (NM_033414), the promoter indicated in moss green (second panel) is selectively used in fetal heart. The upper and lower panels represent the TSS tag usages in adult and fetal tissues, respectively. Height of the vertical bars represents the number of TSS Seq tags located in the corresponding genomic regions. Different alternative promoters are represented by different colors. Each horizontal line represents the experimental condition from which TSS tags were derived. Legends for the tissues and sum of the TSS tag counts are shown at the right margin. (E) Example of the case in which alternative promoter-specific induction was observed in response to IL-4 stimulation in Ramos cells. In the hypothetical protein LOC746 gene (NM_ 014206), the alternative promoter indicated in red (first panel) is selectively induced while the other alternative promoter indicated in blue (second panel) remained unchanged. The indicated TSS regions are magnified to the nucleotide level in the bottom lower panels.

Interface of the updated ‘ncRNA viewer’ and ‘Comparative Genomic viewer’. (A) Example of the TSS tags identified from the surrounding regions of reported small RNAs. The result of the search for a small ncRNA, miR9-2, is shown. Complete cDNA (AK091356) identified in the same region is also represented by orange boxes. (B) Evolutional conservation of the alternative promoters of the protein kinase C zeta (PRKCZ) genes (NM_002744). Different alternative promoters are marked by different colors. Upper and lower panels represent the TSS information in humans and mice, respectively. Corresponding genomic sequences were aligned according to the UCSC Genome Browser information.

Interface of the updated ‘ncRNA viewer’ and ‘Comparative Genomic viewer’. (A) Example of the TSS tags identified from the surrounding regions of reported small RNAs. The result of the search for a small ncRNA, miR9-2, is shown. Complete cDNA (AK091356) identified in the same region is also represented by orange boxes. (B) Evolutional conservation of the alternative promoters of the protein kinase C zeta (PRKCZ) genes (NM_002744). Different alternative promoters are marked by different colors. Upper and lower panels represent the TSS information in humans and mice, respectively. Corresponding genomic sequences were aligned according to the UCSC Genome Browser information.

Also for example, users can search for all alternative promoters with more than a 5-fold induction after IL-4 stimulation in Ramos cells and with expression level >5 ppm. Figure 1E shows the result of such a search. In the hypothetical protein LOC746 gene (NM_014206), second alternative promoter (red) was selectively induced, while the expression level of the other downstream alternative promoter (blue) remained unchanged. It should be noted that expression analysis using microarrays or RT–PCR could miss such promoter-specific expression changes depending on the positions of the designed DNA probes or PCR primers. To the best of our knowledge, there is no database which represents differential usage of each of the promoters under different experimental conditions in a quantitative manner. Recent studies have suggested such diverse transcriptional regulations give molecular basis to produce complex functional network of human genes by a limited number of total genes ( 7– 9). Also, precise identification of the changes in gene expression associated to each alternative promoter is essential to interpret accumulating ChIP-Seq data ( 11), for example, to attribute transcription induction to proximal binding of a particular transcription factor. Updated DBTSS will meet the versatile requirements of the analysis of transcriptional network of human genes in the next generation sequencing era.

TSS information for non-coding RNAs

The TSS information collected in an unbiased-manner throughout the human genome is also useful to identify and characterize hitherto unidentified transcripts. Particularly, the new version of DBTSS can be a unique and important resource for identifying primary transcripts of miRNAs and other non-coding RNAs (ncRNAs) which are located in intergenic regions ( 12). Although hundreds of putative ncRNAs have been identified and their biological characterization undertaken, there have been only few cases in which the TSSs of the primary transcripts the ncRNAs were identified and their promoter structures were elucidated. The new DBTSS contains information of the miRBase database ( 13) and NR transcript information of the RefSeq database ( 14), and TSSs within an arbitrary defined distance from the ncRNAs can be retrieved. As shown in Figure 2A, a possible TSS of a primary transcript of an miRBase miRNA miR9-2 (miRBase http://microrna.sanger.ac.uk/cgi-bin/sequences/mirna_entry.pl?acc=MI0000467) was found in 6 Kb upstream regions of miR9-2. In addition, our cDNA sequence data also suggested that this miRNA exists in the 3′-end terminal region of a putative non-coding transcript, AK091356.

Updated comparative genomics viewer

Although DBTSS now includes an unprecedented amount of data, we were concerned that many promoters and ncRNAs could be products of ‘transcription noise’, which might occur in the human genome, and thus have no biological relevance. In order to address this concern, we updated our comparative genomic viewer so that the users can examine the evolutional conservation of the surrounding genomic sequences and the TSS tag information against other mammals. Figure 2B exemplifies the case in the protein kinase C zeta (PRKCZ) gene (NM_002744). This gene contains at least three alternative promoters as highlighted in blue, red and green. The most upstream and the third promoters (blue and green) are used in fetal kidney and heart, while the second promoter (red) is selectively used in fetal brain. The genomic sequence of the first two promoters (blue and red) are well-conserved between human and mouse and corresponding TSS tags were observed in both species. In contrast, the genomic sequence surrounding the third promoter (green) is not conserved, and this promoter does not seem to be used in mouse. In order to delineate complex transcriptional regulations of this gene, it is essential to consider different usage and different level of the evolutional conservation of each of the promoters as represented here.

Similarly, we examined evolutionary conservation of the intergenic TSCs of >5ppm and found that at least half are not conserved between human and mouse (detailed analysis will be published elsewhere). It is still not clear whether these intergenic clusters of transcription starts correspond to protein coding or non-coding RNAs performing human-specific biological functions or not. But the information provided in the comparative display should give useful clues to prioritize the targets and design future experiments aiming at further functional studies.


Results

Genetic effects on gene expression and DNA methylation

We previously reported the discovery of cis associations between genetic variation and gene expression (expressed Quantitative Trait Loci eQTLs) and between genetic variation and DNA methylation (methylation QTLs mQTLs) in primary fibroblasts, EBV-transformed lymphoblastoid cell lines (LCLs) and primary T-cells of newborn babies, which are shown in Table 1[37]. Here, we have assessed the level of replication of the LCL eQTLs with those LCL eQTLs reported in a more powered RNA-seq study using older cell-lines from adult individuals [5]. This yields a replication of about 70% based on proportion of true positive from a P-value distribution [38] and effect size comparison (S1 Fig.). In this study we have also analyzed the location of the previously discovered eQTLs and mQTLs. As observed in previous microarray studies, highly significant eQTLs cluster close to the TSS [39]. Additionally, when eQTL genes are classified by whether they are called significant in one, two, or three cell-types, we observe that eQTLs significant in all cell-types tend to be less distant to the TSS than eQTLs significant in two cell-types, and these are less distant than those significant in only one cell-type (S2 Fig.). The same pattern is observed for the LCL eQTLs that were replicated in an independent data set, as well as for a similar analysis that deals better with winner’s curse (S2C-D Fig.). This replicates the patterns observed in previous studies [6, 22], and although some eQTLs may be misclassified due to winner’s curse, this pattern may reflect the importance of distant regulatory elements, such as enhancers, in tissue-specific regulation. Additionally, we also find a large proportion of eQTLs very close to the transcription end site (TES S3 Fig.), similar to previous observations [4, 40]. Also confirming previous studies with lower resolution arrays [20], highly significant mQTLs are overrepresented close to the interrogated CpG site (P < 1.3E-14 S4 Fig.). In all cell-types, we observe that the best eQTLs per gene are significantly enriched in DNase I hypersensitive sites, exons and CpG islands (Fig. 1A see also S5 Fig.). Also in all cell-types, mQTLs are significantly enriched in enhancers and insulators, and depleted in last exons and introns (Fig. 1B see also S6 Fig.). Several of these QTLs involve SNPs that have been reported to be associated to various diseases and traits according to literature of genome wide association studies [41] (GWAS)- 8, 3 and 4 eQTLs, and 32, 51 and 74 mQTLs, in fibroblasts, LCLs and T-cells, respectively—although this enrichment is not significant and does not necessarily imply causal relationship between these eQTLs or mQTLs and disease. In conclusion, genetic variants affecting gene expression and DNA methylation levels often overlaps with functional genomic elements. This also indicates that the DNA sequence variation greatly influences the level of methylation. The genetic variants affecting DNA methylation are predominantly located in distant regulatory regions, as shown here, or in non-CpG island promoters as shown before [37], rather than inside genes. These results are compatible with the observations of differential methylation across tissues being predominantly located distant to transcription start sites [31], and enriched for inter-individual methylation variation associated to genetic variation [37].


Watch the video: Fibroblasts In 3 Minutes (December 2021).