Adult mouse fibroblasts retain organ-specific transcriptomic identity

  1. Elvira Forte  Is a corresponding author
  2. Mirana Ramialison
  3. Hieu T Nim
  4. Madison Mara
  5. Jacky Y Li
  6. Rachel Cohn
  7. Sandra L Daigle
  8. Sarah Boyd
  9. Edouard G Stanley
  10. Andrew G Elefanty
  11. John Travis Hinson
  12. Mauro W Costa
  13. Nadia A Rosenthal
  14. Milena B Furtado  Is a corresponding author
  1. The Jackson Laboratory, United States
  2. Australian Regenerative Medicine Institute, Monash University, Australia
  3. Systems Biology Institute Australia, Australia
  4. Murdoch Children’s Research Institute, The Royal Children’s Hospital, Australia
  5. Centre for Inflammatory Diseases, School of Clinical Sciences at Monash Health, Monash University, Australia
  6. Department of Paediatrics, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Australia
  7. Department of Anatomy and Developmental Biology, Monash University, Australia
  8. Cardiology Center, UConn Health, United States
  9. National Heart and Lung Institute, Imperial College London, United Kingdom

Abstract

Organ fibroblasts are essential components of homeostatic and diseased tissues. They participate in sculpting the extracellular matrix, sensing the microenvironment, and communicating with other resident cells. Recent studies have revealed transcriptomic heterogeneity among fibroblasts within and between organs. To dissect the basis of interorgan heterogeneity, we compare the gene expression of murine fibroblasts from different tissues (tail, skin, lung, liver, heart, kidney, and gonads) and show that they display distinct positional and organ-specific transcriptome signatures that reflect their embryonic origins. We demonstrate that expression of genes typically attributed to the surrounding parenchyma by fibroblasts is established in embryonic development and largely maintained in culture, bioengineered tissues and ectopic transplants. Targeted knockdown of key organ-specific transcription factors affects fibroblast functions, in particular genes involved in the modulation of fibrosis and inflammation. In conclusion, our data reveal that adult fibroblasts maintain an embryonic gene expression signature inherited from their organ of origin, thereby increasing our understanding of adult fibroblast heterogeneity. The knowledge of this tissue-specific gene signature may assist in targeting fibrotic diseases in a more precise, organ-specific manner.

Editor's evaluation

The authors aim to show that fibroblasts have a heterogenous transcriptome that is retained throughout their lifetime due to their source of embryonic origin. Of great interest is that compelling evidence is provided that these transcriptional signatures have direct translational consequences. This is shown through coculture experiments, where coculture of cardiomyocytes with non-cardiac fibroblasts impairs integration and contractility, while cardiac fibroblasts integrate with cardiomyocyte cultures to create functional beating tissue. This memory is shown to be malleable: three days post implantation in the renal capsule, explanted fibroblasts largely maintained their original transcriptomic signature, while also showing the onset of adaptation to a new microenvironment. In addition, markers are identified which allow the separation of fibroblasts based on their anatomical origin. Considering the lack of tissue-specific markers for fibroblasts, this is a significant advance.

https://doi.org/10.7554/eLife.71008.sa0

Introduction

Fibroproliferative disorders are the main cause of mortality and morbidity in developed countries, accounting for about 45% of deaths in the United States (Bitterman and Henke, 1991). Despite the impactful prevalence of chronic organ fibrosis, current antifibrotic drugs are both inefficient and nonspecific to this condition (Friedman et al., 2013; Henderson et al., 2020). Fibroblasts, main players in fibrosis, have gained increased attention for their capacity to perform functions far beyond their canonical secretion of extracellular biological scaffolding and formation of scar tissue after injury. Recent literature poses the organ fibroblast as a major regulatory hub that senses local microenvironment imbalances and controls tissue remodeling (Gerber et al., 2018) upon activation and phenotypic differentiation into the profibrotic myofibroblast (Pakshir et al., 2020). They are also involved in immunomodulation (Van Linthout et al., 2014), by producing and responding to cytokines that activate immune cells of the innate and adaptive immune systems (Forte et al., 2018; Boyd et al., 2020), through organ-specific regulatory networks (Krausgruber et al., 2020).

Organ fibroblasts have been historically difficult to identify and study in vivo, due to their vague functional definition and lack of adequate markers that label organ fibroblast pools completely and specifically (Swonger et al., 2016). Recent advances in lineage tracing and multiomics single-cell analyses have revealed a significant heterogeneity of fibroblasts within and among tissues, and we are just beginning to understand how fibroblast heterogeneity correlates with distinct functions (Henderson et al., 2020; Shaw and Rognoni, 2020; Lynch and Watt, 2018; LeBleu and Neilson, 2020; Griffin et al., 2020). Despite being morphologically similar, spindle-shaped mesenchymal cells located in stromal tissues, fibroblasts acquire specialized functions related to their anatomical position (Krausgruber et al., 2020; Slany et al., 2014; Foote et al., 2019). They appear to retain a positional memory of the embryonic developmental axis: anterior–posterior, proximal–distal, and dermal–nondermal, possibly reflecting their role in conveying positional identity in embryogenesis (Rinn et al., 2006; Rinn et al., 2008; Ackema and Charité, 2008; Chang et al., 2002). The retention of positional molecular information through to adulthood suggests organ fibroblasts respond to molecular cues that drive body compartmentalization. Fibroblast heterogeneity within an organ tends to arise from the distinct embryological origin and/or anatomical localization (Lynch and Watt, 2018; Griffin et al., 2020; Muhl et al., 2020; Forte et al., 2020), while interorgan differences have been mostly ascribed to the matrisome, as shown by the transcriptomic comparison among fibroblasts from muscular tissues (Muhl et al., 2020).

Having previously reported that fibroblasts isolated from the adult mouse heart retain a cardiogenic transcriptional program (Furtado et al., 2014a), we show here that fibroblasts isolated from different adult organs similarly retain the expression of transcription factors and other gene sets involved in the determination of organ formation and patterning during embryonic development. This signature is captured in nascent embryonic organ fibroblasts and retained in adult fibroblasts under culture in isolation or in coculture with parenchymal cells from different organs. Further ectopic transplantation of fibroblasts into a different organ in vivo demonstrates the strength of the organ molecular signature despite new microenvironmental challenges. The robustness of the fibroblast organ transcriptome signature shown here supports its importance for organ interaction, connectivity, and function. In addition, knockdown of selected organ development transcription factors in cardiac fibroblasts deregulated the expression of genes involved in inflammation, fibrosis, and extracellular matrix (ECM) deposition, further supporting the relevance of these genes in fibroblasts function. In summary, our study uncovers stable expression of organ-specific, development-related signature genes in adult fibroblasts, thus offering new prospects for possible antifibrotic therapies.

Results

Cell homeostasis and ECM components comprise a generic fibroblast gene signature

To compare the gene signature of fibroblasts from different organs and eliminate potential RNA contaminants from other organ cell types, dissociated adult murine tissues were cultured for 5 days, followed by sorting for CD45−CD31−CD90+ fibroblasts (Furtado et al., 2014a; Figure 1—figure supplement 1a). High-throughput gene expression profiling identified 1281 highly expressed genes common to all fibroblast types, comprising the generic fibroblast signature (Figure 1—source data 1).

Through Ingenuity Pathway Analysis (IPA, Qiagen), we classified the commonly expressed genes based on cellular function (Figure 1—figure supplement 1b) and cellular localization (Figure 1—figure supplement 1c). Top functions included mechanisms of cell maintenance, such as proliferation, cytoskeletal arrangement, and cell movement, as well as general metabolic processes, including carbohydrate, nucleic acid protein, and small molecule biochemistry. Common fibroblast identifier genes were encountered within various IPA process classification groups. As an example, the cell surface receptor CD90 (Thy1 gene) belongs with cellular assembly and organization, growth and proliferation and protein synthesis, while the myofibroblast marker smooth muscle actin (Acta2 gene) was found in functions of cellular movement. The presence of CD90 and absence of CD31 (Pecam1 gene)/CD45 (Ptprc gene) in all organ groups validated our positive/negative selection strategy for cell isolation, indicating a generally consistent population of cells in all organs. ECM elements, including collagens, were included in several functional annotations, such as to cell morphology, assembly and organization, cellular compromise, function, and maintenance or cell signaling.

Organ fibroblasts retain Hox codes

The Hox code defines body segmental identity and is highly conserved from flies to mammals. Hox genes show colinear expression and undergo chronological activation in the embryo, where upstream genes successively activate downstream genes in an anteroposterior fashion, such that upstream genes are activated first in more anterior segments of the body. In mammals, the Hox cluster has undergone a series of duplications and deletions that led to the formation of four paralogous clusters a, b, c, and d (Figure 1a).

Figure 1 with 2 supplements see all
Positional code of organ fibroblasts.

(a) Murine Hox cluster code, showing proximally expressed Hox genes in pink and distal ones in purple. (b) Bar plots showing the average raw signal for all expressed Hox genes in organ-specific fibroblast samples. Data are mean ± standard error of the mean (SEM) of three biological replicates for each fibroblast type from the microarray analysis. Refer to Figure 1—figure supplements 12, Figure 1—source data 1, and Figure 1—source data 2.

Figure 1—source data 1

Microarray data: highly expressed genes common to all organ fibroblast populations, classified based on cellular process or cellular localization.

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Figure 1—source data 2

Microarray data: average raw expression and standard errors of Hox code genes across all fibroblast samples from the microarray analysis (n = 3).

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Site-specific HOX expression has been previously reported in human skin fibroblasts (Rinn et al., 2006; Rinn et al., 2008; Chang et al., 2002) and mouse mesenchymal cells isolated from different organs (Ackema and Charité, 2008), and it has been shown to be cell autonomous and epigenetically maintained, suggesting a source of positional memory to differentially pattern tissue-specific homeostasis and regeneration. To determine if fibroblasts isolated from other adult mouse organs retain a distinct Hox signature, we plotted the average raw expression of all Hox genes per each fibroblast type (Figure 1b, Figure 1—figure supplement 2, Figure 1—source data 2). Among profiled organs, five patterns of Hox expression were identified: lung and liver showed expression of anterior Hox genes, in particular genes from the clusters 1–7, although liver had lower Hox4–7 expression when compared with lung. A second group including skin and gonad displayed high Hox6 expression, with skin from thoracic and abdominal ventral skin areas also expressing high Hox9 gene levels. The third classification group was represented by the heart, characterized by low Hox gene expression. This may reflect embryonic developmental processes, as Hox genes are known to exert minimal influence on heart formation, and are generally not expressed in the heart, except for the residual expression carried over by neural crest cells that invade the arterial pole of the heart and promote aorticopulmonary septation (Kirby et al., 1983). The great vessels were excluded from our sample collection, and therefore cells of neural crest origin were likely not captured in the analyses. The fourth classification group was represented by the kidney, which expressed intermediate to high levels of most anterior Hox genes up to Hox11, consistent with previous observations for the developing kidney (Patterson and Potter, 2004). The fifth category, represented by the mouse tail, had a posterior Hox code signature, represented by Hox13, which correlates with previous findings for human distal segment fibroblasts, represented by feet skin fibroblasts (Rinn et al., 2006; Rinn et al., 2008). Taken together with previous observations, these analyses confirm that adult organ fibroblasts retain positional Hox gene expression signatures, generally reflecting the embryological segmental identity of organ fibroblasts.

Organ fibroblasts show unique molecular signatures

To highlight the unique transcriptomic signatures of these positionally distinct fibroblast pools, we performed a differential expression analysis and considered genes that were enriched by tenfold change or more in single organ fibroblasts relative to tail fibroblasts (Figure 2, Figure 2—source data 1). Gene ontology (GO) annotation revealed organ development programs; processes such as epithelial development, hepatoblast differentiation, lung lobe development, kidney development, reproductive process, and heart development were found enriched in their respective organ fibroblast pools (Figure 2a). Strikingly, signature embryonic transcription factors, that is, genes with established involvement in organ development, were enriched in organ-specific subsets, including Tbx20, a crucial transcription factor for heart development previously described in cardiac fibroblasts (Furtado et al., 2014a). Likewise, genes essential for lung morphogenesis (Foxf1) (Costa et al., 2001), liver development (Hhex) (Keng et al., 2000), early kidney formation (Pax8) (Bouchard et al., 2002), and gonad development (Lhx9) (Birk et al., 2000) were all specifically enriched in fibroblasts from their respective organs. Expression of signature genes was validated by quantitative polymerase chain reaction (qPCR) (Figure 2b, Figure 2—figure supplement 1) and immunocytochemistry (Figure 3, Figure 3—figure supplement 1). In general, signature gene expression patterns in embryonic fibroblasts were retained in fibroblasts from adult tissues (Krt4, Krt6a, Serpinb5, and Hp for skin; Tbx20 and Col2a1 for heart; Foxf1 for lung; Hhex and Foxa2 for liver, Bmp7 and Pax8 for kidney, Cyp11a1 and Lbx9 for gonad). Significant expression of Hhex and Bmp7 were also found in several organs during embryonic development but were restricted to a single organ in adulthood. As an exception to single organ enrichment, Foxa2 was also substantially upregulated in lung fibroblasts (~20-fold), in addition to liver fibroblasts (~30-fold).

Figure 2 with 7 supplements see all
Embryological molecular signature of organ fibroblasts.

(a) Cytoscape representation of network of genes (dots) singularly expressed in organ fibroblasts (only one gray edge between the gene and the organ) or shared among organs (multiple gray edges linking the gene to several organs). Genes involved in organ development are highlighted. (b, c) Validation of the expression of selected organ-enriched, developmental related genes using qPCR on cultured organ-derived fibroblasts isolated from adult mice (top row) or E16.5 embryos (bottom row). Data are mean ± standard error of the mean (SEM) of the e−DDCt values, on three technical replicates of merged biological samples. The housekeeping gene is Hprt and the reference sample is tail fibroblasts. Refer to Figure 2—figure supplements 17, Figure 2—source data 1.

Figure 2—source data 1

Microarray data: expression of genes that were enriched by tenfold change or more in single organ fibroblasts compared to tail fibroblasts (n = 3).

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Figure 3 with 1 supplement see all
Adult fibroblasts express organ-specific transcription factors.

Immunocytochemistry for development-related organ-enriched markers (KRT14, TBX20, FOXA2, HHEX, FOXD1, PAX8, and RSPO) on adult fibroblasts obtained from different organs of Col1a1-GFP mice and cultured for 5 days: (a) Skin fibroblasts stained with a mouse monoclonal anti-KRT14 antibody; (b) heart fibroblasts stained with a mouse monoclonal anti-TBX20 antibody; (c) lung fibroblasts stained with a rabbit monoclonal anti-FOXA2 antibody; (d) liver fibroblasts stained with a rabbit monoclonal anti-HHEX antibody; (e) kidney fibroblasts stained with a rabbit polyclonal anti-FOXD1 antibody; (f) kidney fibroblasts stained with a mouse monoclonal anti-PAX8 antibody; (g) testis fibroblasts stained with a goat polyclonal anti-RSPO1 antibody. As secondary antibodies, goat anti-mouse Alexa Fluor 568 was used for (a, b), (f); goat anti-rabbit Alexa Fluor 555 for (c, d) and donkey anti-goat Alexa Fluor 568 for (f). Representative images from three independent experiments on three biological replicates. Scale bar = 100 µm. Refer to Figure 3—figure supplement 1.

IPA analysis delineated top canonical pathways, diseases, functions, and networks associated with selectively enriched genes in each fibroblast population and supported the argument that fibroblasts retain molecular identity of their developmental organ of origin (Figure 2—figure supplements 27). Among organ-related processes enriched in fibroblast subsets were dermatological diseases and conditions and morphogenesis of the epithelial tissue for skin fibroblasts (Figure 2—figure supplement 2), respiratory system development for lung fibroblasts (Figure 2—figure supplement 3), liver development for liver fibroblasts (Figure 2—figure supplement 4), acute renal failure, metanephros development and kidney formation, and abnormal kidney development, disease and function for kidney fibroblasts (Figure 2—figure supplement 5), reproductive system development, function and disease, morphology of genital organs, and primary sex determination networks and reproductive system dysfunction for gonad fibroblasts (Figure 2—figure supplement 6), cardiovascular disease development and function, cardiac enlargement and disease, and cardiac developmental processes for heart fibroblasts (Figure 2—figure supplement 7).

To establish the translational relevance of our findings, the presence of 42 genes uniquely enriched in cardiac fibroblasts (log2, tenfold, False Discovery Rate (FDR) < 0.01) was determined in left ventricular heart biopsies from healthy (N = 5) and chronic ischemic heart failure patients (N = 5) (Figure 2—figure supplement 7—source data 1). Ischemic heart failure was chosen for analysis due to the likelihood of replacement fibrosis as a pathological signature. Out of the 42 murine cardiac fibroblast genes, 28 were present in both control and heart failure samples, including Tbx20, which was unchanged between control and heart failure. FNDC1, FRZB, MFAP4, and OLFML1 were significantly upregulated in ischemic heart failure; while MFSD2A, PNP, and SERPINA3N were downregulated. Four genes had no human homolog and ten were not found in the human heart transcriptome dataset. These findings confirm general commonalities across species and further identify potential candidates of fibrotic cardiovascular disease interest for future investigation.

Organ-enriched gene expression is retained at the single-cell level in freshly isolated and cultured fibroblasts

Single-cell RNAseq (scRNAseq) is a powerful tool to determine granularity of gene expression at the population level. To assess how organ signatures are reflected in freshly isolated fibroblasts, we reanalyzed the stromal cell dataset from a publicly available multiorgan scRNAseq study (the Mouse Cell Atlas) (Han et al., 2018). Focusing on lung, testis, kidney, liver, and neonatal heart cells, we unbiasedly identified eight populations, including three lung and two kidney subclusters (Figure 4a). Pairwise differential expression analysis supported a previously reported classification of lung fibroblasts populations (Xie et al., 2018), with two types of matrix fibroblasts – ‘Lung A’ (Col14a1, Pi16, Dcn enriched), ‘LungB’ (Col13a1, Cxcl14, Tcf21 enriched) – and a group of myofibroblasts – ‘LungC’ (Acta2, Myl9, Tagln positive) (Figure 4—figure supplement 1 a, b). For kidney clusters, ‘Kidney B’ showed higher levels of canonical fibroblast markers Dcn, Gsn, and Col1a2, while the larger ‘Kidney A’ expressed relatively higher levels of genes involved in response to injury, or in renal carcinoma metastasis and progression (Spp1 and Krt8), suggesting that this cluster is composed of tubular cells acquiring a mesenchymal phenotype in vivo (Rudman-Melnick et al., 2020; Figure 4—figure supplement 1c, d), as kidney epithelial cells are known to undergo dedifferentiation in vivo and in vitro to repair tubular injuries (Kusaba et al., 2014; Van der Hauwaert et al., 2013).

Figure 4 with 2 supplements see all
Analysis of organ-specific signatures at the single-cell level.

(a–c) Reanalysis of the Mouse Cell Atlas stromal cell dataset. (a) Uniform Manifold Approximation and Projection (UMAP) visualization of selected stromal cell populations (682 cells). (b) Heatmap representing the expression of top-organ-specific genes, identified from the bulk RNAseq comparison, on individual cells in the single-cell RNAseq (scRNAseq) dataset. (c) Heatmap showing the average expression per population of organ development-related genes (same as shown in Figure 3). (d–f) scRNAseq analysis of mixed cultured stromal cells of different origins. (d) UMAP visualization of the captured cells. (e) Heatmap representing the expression of top-organ-specific genes, identified from the bulk RNAseq comparison, on individual cells. (c) Heatmap showing the average expression per population of organ development-related genes (same as shown in Figure 3). Refer to Figure 4—figure supplements 12, Figure 4—source data 1, and Figure 4—source data 2.

Figure 4—source data 1

Analysis of the stromal cell aggregate from the Mouse Cell Atlas: markers genes per each population, and markers identified by pairwise comparison of the two kidney and three lung populations.

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Figure 4—source data 2

Analysis of in-house single-cell RNAseq (scRNAseq) data of merged cultured fibroblasts from different organs: markers genes per each population, and markers identified by pairwise comparison of the two kidney and two cardiac populations.

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Overall, the expression of organ-specific (Figure 4b) and developmental related genes (Figure 4c) previously identified in the bulk cultured cell analysis was preserved, despite the reduced coverage and expected heterogeneity at the single-cell level (Figure 4b). Interestingly, when multiple subclusters were present (as in lung and kidney fibroblasts) the expression of the organ-specific genes was enriched in myofibroblasts (Lung C expressed relatively higher levels of Foxf1) or activated fibroblasts (Kidney A expressed higher levels of Pax8 and Wnt7b) (Figure 4c, Figure 4—figure supplement 1, Figure 4—source data 1).

Further confirmation of the organ-enriched program was obtained with scRNAseq of pooled primary cultures from different origins (kidney, liver, lung, heart, skin, testis, and tail) (Figure 4d). Unbiased clustering defined Kidney 1–2, Lung, Heart 1–2, and Tail fibroblasts. Two additional clusters were unclassified based on organ identity, although marked by the expression of Hoxc genes (HoxGenes) and proliferation genes (Prolif + HoxGenes) (Figure 4d–f, Figure 4—source data 2). Highly expressed (Figure 4e) and development-related genes (Figure 4f) from original bulk analysis were again confirmed in these organ populations. Both cultured kidney clusters (Kidney 1–2) expressed the epithelial stress response marker (Spp1) and were transcriptionally closer to freshly isolated ‘Kidney A’ (Figure 4—figure supplement 2c), possibly representing two stages of tubular cells epithelial-to-mesenchymal transition (Iwano et al., 2002): Kidney1 had higher expression of myofibroblast genes (Col4a1, Tagln, Myl9, and Sparc) and the kidney-fibroblast-enriched gene Pax8; Kidney 2 strongly expressed epithelial genes (Krt7, 8, 18, Epcam, and Clu) (Figure 4—figure supplement 2d, e; Figure 4—source data 2). As for the cultured heart fibroblasts, Heart 1 displayed myofibroblast genes (Acta2, Tagln, and Myl9) and Heart 2 had enhanced signature of injury response/acutely activated fibroblasts (Mt1, Ccl2, Clu, and Dcn) (Figure 4—figure supplement 2a-b; Figure 4—source data 2; Forte et al., 2020). Overall, scRNAseq experiments showed that cultured cells present an activated/myofibroblast-like phenotype compared to freshly isolated cells and confirmed the retention of an organ-specific core transcriptome identity on both cultured and freshly isolated cells.

Organ-enriched transcriptome is involved in the fibrotic response

To investigate the functional relevance of organ-enriched fibroblast transcriptomes, a CRISPR knockdown approach was used to downregulate core organ transcription factors, taking the heart as a model (Figure 5). ROSACas9-GFP (Platt et al., 2014) adult cardiac fibroblasts were cotransfected with mCherry mRNA and GFP guide RNAs for determination of transfection and knockdown (KD) efficiency, respectively (Figure 5a). After 72 hr of transfection, mCherry was observed in roughly 67% of cells (Figure 5b) by flow cytometry, and GFP mRNA expression was downregulated by over 90% when compared with scrambled guides (negative control) (Figure 5c). GFP fluorescence was also dramatically decreased by 72 hr (Figure 5a; Li et al., 2014).

In vitro knockdown of core cardiac transcription factors.

(a) Images of adult cardiac fibroblasts derived from ROSACas9-GFP mice, 24 and 72 hr post-transfection with two guide RNAs for GFP and CleanCap mCherry mRNA. (b) Flow cytometry plot showing the expression on mCherry in 67% of transfected cells (representative image of three independent experiments). (c) Relative quantification qPCR of GFP, normalized by Gapdh expression, in cells transfected with scrambled or GFP guide RNAs. (d, e) Volcano plots showing genes differentially expressed in Gata4 (left) and Tbx20 wns (right). Fold change in expression of Gata4 (left) and Tbx20 (bottom) in cells transfected with specific gRNAs versus scramble RNA, quantified through RNA sequencing. (f) Venn diagram showing the overlap among genes affected by Gata4 (Gata4KD) or Tbx20 (Tbx20KD) knockdown and number of genes upregulated by tenfold or more in heart fibroblasts (eHF) compared to other organs. (g) Plot showing changes in expression of eHF genes affected by Gata4 (in blue) or Tbx20 (in red) knockdown. (h) Genes regulated by both Gata4 and Tbx20 in opposing manner. (i) KEGG pathway analyses. Top panel: 58 genes affected in both Tbx20 and Gata4 knockdown; middle panel: 594 genes affected by Gata4 knockdown; bottom panel: 66 genes affected by Tbx20 knockdown. Blue – pathway changed in opposite directions, red – upregulated pathways, and green – downregulated pathways. (j) Hand-picked genes illustrate alterations in processes known to affect the cardiac fibrotic response for Gata4 or Tbx20 knockdown. All data are represented as fold changes over scrambled control (average ± standard error of the mean [SEM]; d–j) from bulk RNAseq of three biological replicates per condition. Selected significant genes have an FDR < 0.05. Refer to Figure 5—source data 1.

Figure 5—source data 1

CRISPR-Cas9 experiments: sequence of the guide RNAs; differentially expressed genes between Tbx20KD and Gata4 KD and corresponding controls.

KEGG pathways analysis.

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With this confirmation, Gata4 and Tbx20, core transcription factors essential for heart formation in embryonic development (Nim et al., 2015), were knocked-down in cultured ROSACas9-GFP cardiac fibroblasts, followed by bulk RNAseq analysis (Figure 5d–j). Gata4 is expressed by all organ fibroblasts while fibroblast Tbx20 expression is restricted to the heart. Despite similar KD efficiencies for both targets (~60%; Figure 5d, e), Gata4 KD induced a higher number of dysregulated genes (red dots on volcano plots) compared with Tbx20 KD. Among genes upregulated by tenfold in the original bulk organ analysis (eHF; Figure 5f, g), nine were dysregulated by Gata4 KD and five by Tbx20 KD. Only two of these genes were dysregulated in both conditions, including Tbx20, suggesting Gata4 as a possible upstream regulator of Tbx20. A number of genes showed opposite regulation between Gata4 KD and Tbx20 KD (Figure 5h), confirming the specificity of KD response. These included cytokines and cytokine receptors (Il11, Tnfsf18, and Ackr4), genes involved in infection (Ptgs2 and Heyl), cell adhesion and migration (Spon2 and Mmp10). Of note, among the genes selectively upregulated in Gata4 KD, Il11 is a key mediator of organ fibrosis, possibly downstream of TGFβ (Schafer et al., 2017). Among genes upregulated in Tbx20 KD, the downstream effector of Notch signaling Heyl is involved in cardiogenesis and is thought to repress Gata4 expression (Fischer et al., 2005), Mmp10 is upregulated in patients with end-stage heart failure (Wei et al., 2011), and it is involved in valve ossification (Matilla et al., 2020). KEGG pathway analyses (Figure 5i, Figure 5—source data 1) confirmed the involvement of Gata4 and Tbx20 in common but also diverse pathways. The top pathways uniquely upregulated by Gata4 KD included Akt signaling, ECM–receptor interaction and renin secretion, implicating Gata4 in the modulation of cardiac fibroblast growth and function (Brilla et al., 1995; Li et al., 2015; Dai et al., 2019). The top pathways upregulated by Tbx20 KD involved IL-17 and relaxin signaling, as well as transcription misregulation in cancer. IL17 has been shown to regulate the fibrotic response in proinflammatory conditions such as psoriasis and pulmonary/liver fibrosis (Lei et al., 2016; Zhang et al., 2019; Seki and Brenner, 2015; Blauvelt and Chiricozzi, 2018), while relaxin has a well-established role in suppressing myofibroblast activation and ECM remodeling (Ng et al., 2019; Martin et al., 2019; Samuel et al., 2017).

In summary, both gene KDs affected matrix components and modulators (Figure 5j), as well as cell adhesion, cell–cell communication, and cell signaling genes. Markers of the epicardium, the external layer of the heart from which embryonic fibroblasts derive (Quijada et al., 2020), were also modulated, as well as several myocardial genes, found in low levels in cardiac fibroblasts and downregulated in Gata4 KD. These results confirm the biological relevance of organ-specific fibroblast gene expression.

Organ fibroblast specificity affects tissue function in coculture systems

The studies described above confirmed that fibroblasts retain an organ-specific transcriptome from embryonic development to adulthood, and that their identities are largely maintained in cultured cells, suggesting that fibroblast transcriptomes may be important for in vivo organ function. To determine whether the source of organ fibroblast affects organ function, 2D and 3D cocultures of cardiomyocytes (CMs) with adult kidney and cardiac fibroblasts were performed (Figure 6).

Adult fibroblasts retain tissue-specific function in vitro: impaired neonatal cardiomyocyte beating in presence of kidney-derived fibroblasts.

Immunocytochemistry for TnT on 2D coculture of neonatal ventricular cardiomyocytes (NCM, TnT+ in red) with either adult cardiac fibroblasts (HF) in (a) or kidney fibroblasts (KF) in (b), isolated from Col1a1-eGFP mice (in green). Nuclei are labeled with 4′,6-diamidino-2-phenylindole (DAPI) (in blue). The right panel shows colocalization of the two cell types (green+ and red+) in white. (c) Quantifications of the percentage of cardiomyocytes per culture condition (top), beating of the 2D cultures expressed in beats per second (bottom left) and percentage of colocalization (bottom right). (d) Confocal Z-stack images reconstructed with Imaris, of cardiac microtissues constituted of 85% hiPSCs derived cardiomyocytes (iCM) and 15% of either cardiac (HF) or kidney (KF) fibroblasts, stained for TnT (in red, left panels) or MF20 (in red right panels). The adult fibroblasts were isolated from Col1a1-eGFP mice (in green), nuclei stained with DAPI (in red). Forty-eight organoids were generated per each fibroblasts cell type. Scale bar = 50 μm. All data in (c) are mean ± standard error of the mean (SEM) on three independent experiments, p values were calculated by two-sample t-test; * = p<0.05, *** = p<0.005 Refer to Video 1 and Video 2.

For 2D cultures, neonatal ventricular CMs were plated with Col1a1-GFP+ fibroblasts isolated from the adult kidney or heart (Kanisicak et al., 2016). Within 24 hr, coculture with adult kidney fibroblasts almost completely impaired CM contractility (Figure 6b, c, Video 1), although the number of CMs present in both cultures was not significantly different. Conversely, cardiac fibroblast coculture resulted in a syncytium of cells beating in synchronism (Video 2) at a relatively lower pace than neonatal CMs alone (Figure 6c), possibly reflecting an effect of adult HF on neonatal CM maturation (Wang et al., 2020). In addition, cardiac fibroblasts were well integrated with neonatal CMs, as shown by the percentage of colocalization, while kidney fibroblasts and CMs seemed to repel each other. To confirm these findings, 3D cardiac microtissues we generated, as previously described (Boudou et al., 2012; Hinson et al., 2015). A suspension of 85% human-induced pluripotent stem cell-derived CMs (iCMs) and 15% adult cardiac or kidney fibroblasts was loaded on millitissue devices with pairs of cantilevers to generate force. As expected, cardiac fibroblasts were homogeneously interspersed, while kidney fibroblasts were aggregated to the center or periphery of the organoids (Figure 6d), indicating lack of integration between the two cell types. These results implicate organ-specific fibroblasts in imparting their cognate tissue integrity.

Video 1
Coculture of adult cardiac fibroblasts with neonatal ventricular cardiomyocytes.

×20 magnification.

Video 2
Coculture of adult kidney fibroblasts with neonatal ventricular cardiomyocytes.

×20 magnification.

Ectopically transplanted fibroblasts retain core transcriptional identity

To investigate if adult fibroblasts maintain their organ-specific signature when exposed to a different tissue microenvironment in vivo, ROSAmT/mG fibroblasts from tail, heart, and kidney were absorbed on surgical gel foam and transplanted under the kidney capsule of syngeneic C57BL6/J mice (Figure 7a). Three days post-transplantation, kidneys were dissected and sorted (Figure 7b) to determine transcriptional changes in transplanted fibroblasts. Transplanted heart (HFs), kidney (KFs), tail (TFs) fibroblasts and corresponding in vitro cultured controls (HFc, KFc, TFc) were processed for bulk RNAseq. Multidimensional scaling plot for all samples showed that the three organ fibroblast types retained a distinct identity post-transplant, despite a reduced transcriptomic separation (Figure 7c).

Figure 7 with 1 supplement see all
Fibroblast tissue-specific response to in vivo ectopic transplantation under the kidney capsule.

(a) Representative images of a dissected kidney to highlight the area where TdTomato+ cells were transplanted; brightfield image on the left; brightfield overlaid with the fluorescence image acquired in the red channel on the right. (b) Flow cytometry plot showing the gating strategy used to isolate live CD45−;TdTomato+ cells 3 days post-transplantation in the kidney capsule. (c) Multidimensional scaling plot calculated on the top 500 genes postnormalization to visualize the transcriptomic similarity among all samples. (d–h) Comparison of HF and KF gene expression to TF in culture (HFc/TFc, KFc/KFc) and post-transplant in the kidney capsule (HFs/TFs, KFs/KFs). (d) Heatmap showing expression of significantly regulated eHF genes in the four conditions. (e) Heatmap showing the expression of eKF genes in the four conditions. (f) Dot plot indicating GO terms associated with cardiac development, identified from the DAVID database analysis of HFs or HFc-enriched genes. (g) Dot plot indicating the GO terms associated with cardiac development, identified from the DAVID database analysis of KFs or KFc-enriched genes. (h–k) Analysis of the differential expression by experimental condition: transplanted cells versus cells in culture HFs/HFc, TFs/TFc, and KFs/KFc. (h) Venn diagram showing the significant differentially regulated genes (FDR < 0.05) in the three comparison sets. Only genes with a logFC >1 or <−1 were considered. (i) Heatmap of differentially regulated Hox genes. (j) Bar plot of the KEGG pathway analysis on the common regulated genes (light blue – downregulated, dark blue – upregulated), and genes uniquely modulated in KF (dark green – upregulated, light green – downregulated), HF (red – upregulated, brown – downregulated), and TF (black – upregulated, gray – downregulated). (k) Heatmaps showing the top 10 upregulated and top 10 downregulated genes for each dataset. In bold are the genes shared in two different sets of comparisons. Data are means of three biological replicates per condition. All the heatmaps (d, e, j, k) The average log fold change. For (d, e) Genes were ordered based on the FDR (smaller to larger value) of the comparison in the first column, HFc/TFc and KFc/TFc, respectively. Dot plots (f, g) and bar plot (j) data were organized based on the log transformation of the p values (−log(p value)). eKF – kidney-fibroblast-enriched genes, same as shown in Figure 2—figure supplement 5, eHF – heart-fibroblast-enriched genes, same as shown in Figure 2—figure supplement 7, KFs – transplanted kidney fibroblasts, HFs – transplanted heart fibroblasts, TFs – transplanted tail fibroblasts, KFc – kidney fibroblasts in culture, HFc – heart fibroblasts in culture, and TFc – tail fibroblasts in culture. Refer to Figure 7—figure supplement 1.

To assess eventual changes in organ-specific identity, we compared the expression of sorted and cultured heart and kidney fibroblasts relative to tail fibroblasts, and we analyzed the expression of heart-enriched (eHF) and kidney-enriched (eKF) genes identified from the initial bulk RNA analyses (Figure 2—source data 1, Figure 2—figure supplements 27). We observed that fibroblasts generally maintained their core identity after transplant. Of the 26 genes enriched in HFc, 21 (80.7%) were similarly modulated in HFs, only two were downregulated and three were not detected (Figure 7d). As expected, eHF gene expression was low or downregulated in KFc compared to TFc and kept a similar expression pattern post-transplant (Figure 7d). Of the 47 eKF significantly expressed in KFc, 41 (87.2%) were modulated in the same direction in KFs, one gene was downregulated, and five genes were not detected. Twenty-six (55.3%) eKF genes were also found in HFc, 17 mildly upregulated and 9 downregulated. Of these, 14 were similarly regulated in HFs, 1 gene was not detected and 11 were differentially regulated (7 upregulated, 4 downregulated). An additional 13 eKF genes were detected in HFs, all mildly upregulated except for one downregulated, showing an adaptation to the new microenvironment (Figure 7e). GO analysis of KF- or HF-enriched genes in culture or post-transplant using DAVID (Database for Annotation, Visualization and Integrated Discovery, Dennis et al., 2003) revealed terms related to organ development, in line with the previous observations (Figure 7f, g). The top GO terms for both HFc and HFs were related to cardiac morphogenesis and cardioblast differentiation; the top terms for KFc and KFs were related to mesonephros and metanephros development. In summary, both HFs and KFs maintained their core transcriptomic identity compared to TFs.

Ectopically transplanted fibroblasts adapt to a new microenvironment

To analyze the differential HF, KF, and TF responses to the transplantation, gene expression of each post-transplant fibroblast type was compared with its equivalent control kept in culture. Despite the retention of organ identity signatures, numerous genes were modulated in transplanted fibroblasts compared to cultured controls (4082 genes for HFs/HFc, 3555 for KFs/KFc, and 2750 for TFs/TFc) (Figure 7h). These included 4–5 Hox genes per cell type, showing a modulation of the cell-type-specific positional code. Hoxa1 (the highest expressed in HFc) and Hoxc13 were downregulated in all conditions (Figure 7i). The tail-enriched Hoxb13 was downregulated in TFs, while Hoxa5 was increased. Hoxd3 and Hoxd8 were upregulated in both HFs and KFs; and Hoxd10 was downregulated in KFs. Interestingly, Hoxd8 is important for the maintenance of epithelial phenotype in adult kidney and is expressed in the ureteric bud during development (Di-Poï et al., 2007), while Hoxd10 is diffusely expressed in kidney mesenchyme in embryos; both Hoxd10 and Hoxd3 regulate Itga3 expression and have been involved in different types of cancer (Hua et al., 2020). The differential regulation of Hoxd10 and Hoxd3 may suggest the acquisition of a cortex-like phenotype by transplanted KFs.

KEGG analysis of the genes modulated in response to the transplant showed upregulation of pathways related to fibrosis and damage response (TNF signaling, ECM–receptor interaction, protein digestion, and absorption) and downregulation of oxidative phosphorylation, steroid biosynthesis, and p53 signaling among the common regulated genes (Figure 7l, Figure 7—figure supplement 1a). Genes uniquely upregulated in KFs/KFc were related to homologous recombination and cell cycle, with pathways including several histone genes (Alcoholism, Systemic lupus erythematosus, Figure 7—figure supplement 1a); those selectively upregulated in HFs/HFc were associated with cell migration (axon guidance), vasopressin regulated water absorption and lipid metabolism; and to proinflammatory pathways for TFs/TFc (TNF signaling, cytokine–receptor interactions, pathways in cancers) (Figure 7l). Similarly, IPA analysis revealed that the most significantly affected canonical pathways were related to fibrosis (hepatic fibrosis signaling, GP6 signaling); cell migration (Axonal Guidance Signaling); acute phase response, inflammation, and cholesterol biosynthesis (Figure 7—figure supplement 1b). Interestingly, Cardiac Hypertrophy Signaling (including profibrotic signals AngII, TGFβ, and IGF1) and HIF1a signaling were predicted to be downregulated in KFs and upregulated in HFs and TFs, possibly inferring a better resilience of KFs to the kidney capsule environment.

Among the top 10 upregulated genes in transplanted fibroblasts, 6 were shared by the ectopically transplanted HFs and TFs, including the serum amyloid A Saa3, secreted during the acute phase of inflammation (Forte et al., 2020); two metabolic enzymes, Slc27a2, primarily expressed in kidney and liver involved in lipid biosynthesis and fatty acid degradation, and Pck1 key regulator of gluconeogenesis; the cytochrome gene Cy2j5 involved in vasorelaxation (Agba et al., 2020); the kidney abundant protein Kap, androgen-regulated, proximal tubule-specific not expressed at detectable levels in tissue other than the kidney (Toole et al., 1979), Fut9 a fucosyltransferase with the highest expression in adult pancreas, placenta, kidney (Figure 7k). In summary, while transplanted fibroblasts maintained their core identity, they responded to the kidney microenvironment by expressing a subset of kidney-specific genes, modulating positional code genes and activating common and cell-specific pathways in the attempt to adapt to the new, more hypoxic condition.

Discussion

The ability to target-specific organ fibroblasts has long been impaired by the misconception that fibroblasts were functionally and phenotypically homogeneous cells, deputized to synthesizing and organizing the ECM, an idea possibly fostered by a common embryonic origin in the primary mesenchyme (LeBleu and Neilson, 2020). However, recent advances in lineage tracing and single-cell transcriptomic have revealed an extensive intra- and interorgan heterogeneity (Henderson et al., 2020; LeBleu and Neilson, 2020). Multiorgan studies show that B cells (Rocha-Resende et al., 2020), endothelial cells (Paik et al., 2020), fibroblasts (Muhl et al., 2020; Han et al., 2018), and transcriptomes tend to cluster separately based on the organ of origin, suggesting a strong influence of the anatomical location or the local microenvironment on the cell state. A recent study has shown that, despite the likely tissue-specific imprinting, fibroblast subclusters across multiple organs present a common hierarchy with two universal subtypes, with distinct localization within the tissue (Col15a1+ – parenchymal; Pi16+ – adventitial), which in turn generate other more specialized or activated fibroblasts states (Buechler et al., 2021). While our study did not prove or disprove this hypothesis, we were not able to unbiasedly classify our cell subtypes based on this principle. However, our data are in agreement with abovementioned studies that identified subclusters based on organ of origin.

We previously reported that fibroblasts isolated from the adult mouse heart retain a cardiogenic transcriptional program (Furtado et al., 2014a). Here, we compared primary cultures of fibroblasts isolated from organs of different anatomical positions to expand our previous analysis and assess whether development-related genes contribute to the fibroblast interorgan functional heterogeneity. The results of this analysis highlight the presence of an organ-enriched positional code, and the expression of core genes that represent the developmental signature of fibroblast organ origin previously thought to be restricted to the parenchymal component. These molecular profiles are established during embryogenesis, consistent with the fact that organ fibroblasts are not generated from a common progenitor pool but arise independently in different body segments and organs during embryonic development and persist to adulthood.

As in our previous study (Furtado et al., 2014a), we chose to analyze cultured fibroblasts to reduce the risk of contamination from parenchymal cell mRNA. Fibroblast expression patterns in culture were recapitulated in freshly isolated single cells, mostly enriched in activated or myofibroblast-like fibroblast subclusters. These gene signatures can predict the tissue of origin of a mixed population of primary cultured cells analyzed at the single-cell level. Using the heart as a model, we show that signature genes contribute to organ fibroblast function, as evidenced by the deregulation of several profibrotic and proinflammatory genes with knockdown of core transcription factors Gata4 (expressed in all fibroblast types) and Tbx20 (cardiac specific) in cultured adult cardiac fibroblasts. These results place Gata4 upstream of Tbx20, both of which upregulate distinct profibrotic signals, modulate genes involved in extracellular modulation and cell adhesion, and have opposite effects on cytokine–cytokine receptor expression, confirming that the core cardiogenic program in cardiac fibroblasts is involved in regulating their function.

Dermal fibroblasts from different sites of the body have shown different efficiency of reprogramming into induced pluripotent stem cells (Sacco et al., 2019), but not much is known about other fibroblast tissue-specific functions. The coculture studies presented here further reinforce the importance of fibroblast core transcriptomes for specialized organ function: while interspersion of cardiac fibroblasts within CM cultures facilitated the propagation of the electric pulse forming a syncytium, cocultured kidney fibroblasts clustered separately and inhibited CM contraction, both in 2D and 3D assays. These findings carry repercussions to in silico organ bioengineering, where combining the correct match of diverse organ cell types may be essential for proper organ formation. Indeed, human-induced pluripotent stem cell-derived cardiac stromal cells enhance maturation of cardiac microtissues (Giacomelli et al., 2020). In addition, the source and type of organ scaffolding, mainly deployed by fibroblasts, is essential for the recreation of organs in a dish (Taylor et al., 2017).

Previous studies have shown that skin fibroblasts and mesenchymal cells from different organs keep a positional identity (Rinn et al., 2006; Rinn et al., 2008; Ackema and Charité, 2008). For mesenchymal cells, the Hox code was maintained also in culture, although whether this depended on cell-to-cell contact remained to be determined (Ackema and Charité, 2008). Here, we show that adult fibroblasts, isolated from a variety of organs, preserve the expression of Hox genes in culture, but that tissue-specific Hox genes were downregulated after ectopic transplantation under the kidney capsule, suggesting that cellular environment can induce reprogramming of positional codes. Interestingly, control kidney fibroblasts also presented changes in the Hox code after transplantation, possibly reflecting the adaptation to the space between the capsule and the cortex, with the decrease of the mesenchyme gene Hoxd10 and increase of Hoxd3 important for the maintenance of an epithelial phenotype in adult kidney. All three transplanted fibroblast types (heart, tail, and kidney) presented an activated phenotype, involving initiation of the acute phase response, profibrotic signals, and metabolic changes. While transplanted heart and tail fibroblasts showed a clearer activation of proinflammatory pathways, genes associated with cell migration, and HIF1a signaling; transplanted kidney fibroblasts appeared more resilient in their native milieu, with the activation of pathways related to proliferation and upregulation of genes indicative of a more epithelial cortex-like phenotype. Both heart and kidney fibroblasts retained a ‘memory’ of their organ of origin, defined by the resilient expression of the core of development-related genes when compared to tail fibroblast control. It remains to be determined if this memory can be erased by a longer residence in the ectopic microenvironment. In light of recent studies on endothelial cells (Yucel et al., 2020; Jambusaria et al., 2020), we propose that the expression of organ-specific genes, previously thought to be restricted to parenchymal cells, may form the basis for organ cohesiveness and performance.

In summary, adult fibroblasts maintain a lasting blueprint of the organ in which they reside, reflective of their developmental origin, which likely plays a role in the orchestration of the tissue-specific homeostasis and reparative response. Exploiting the organ-specific properties of fibroblasts may be a valuable strategy for the targeted control of organ fibrosis, an integral feature of organ failure and disease progression affecting a multitude of pathologies.

Materials and methods

Mice

All experiments were performed with young adult (8–12 weeks old) C57BL/6J, Gt(ROSA)26Sortm1.1(CAG-cas9*,-EGFP)Fezh/J (ROSACas9-EGFP), ROSAmt/mg (JAX Stock# 007576)(Muzumdar et al., 2007), Col1a1-GFP (Yata, 2003) male mice and Col1a1-GFP E16.5 embryos. All animal experimentation conformed with local (Jackson Laboratory) and national (NHMRC and NIH) guidelines.

Fibroblast isolation and sorting

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Liver, heart, lung, kidney, tail, gonad, and ventral skin of adult mice and E16.5 embryos were dissected and finely minced. Fibroblasts were isolated using enzymatic digestion with 0.05% Trypsin/Ethylenediaminetetraacetic acid (EDTA)(Gibco) under agitation at 37°C for 30–40 min. Cells were spun and plated in 10 cm² dishes and cultured to semiconfluence in Dulbecco's Modified Eagle Medium (DMEM) (Thermo Fisher) high glucose supplemented with 10% Fetal Bovine Serum (FBS) (Thermo Fisher), 1% sodium pyruvate (Thermo Fisher), 1% penicillin–streptomycin (10,000 U/ml) (Thermo Fisher), 1% GlutaMAX Supplement (Thermo Fisher) in a 5% CO2 incubator at 37°C. Passage 0 cells were then trypsinized using TrypLE (Thermo Fisher) and further processed for flow cytometry, labeled using CD90-AF647 (BioLegend), CD45-PeCy7, and CD31-Pe (eBioscience) in 2% FBS in Hank's balanced salt solution (HBSS) (Thermo Fisher) and sorted using Influx or Aria II Sorter (BD). The CD90+; CD45−; CD31− fraction was collected for mRNA isolation (Figure 1—figure supplement 1). Adult fibroblasts from ROSACas9-EGFP and Col1a1-GFP were sorted using CD90-APC (BioLegend), CD45-APCCy7(BioLegend), and CD31-PECy7 (BD) after 3 or 5 days, respectively.

Microarray assay

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Sorted organ fibroblasts were resuspended in Cell Lysis buffer, further processed for total RNA isolation using the RNAqueous Micro kit (Thermo Fisher) and DNAse digested on column. Fibroblasts from individual mice were used for each replicate. Triplicates or more were used for each organ. Samples were further processed by the Monash Health Translational Precinct Medical Genomics Facility and ran on Agilent SurePrint G3 mouse gene expression arrays (single color).

Bulk RNA sequencing

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Total RNA was isolated from heart tissue using miRNeasy Mini kit (Qiagen); from cultured fibroblasts (CRISPR experiment) and sorted fibroblasts (kidney capsule experiment), using RNeasy Micro kit (Qiagen) according to the manufacturer instruction and including the optional DNase digest step. Sample concentration and quality were assessed using the Nanodrop 2000 spectrophotometer (Thermo Scientific) and the Total RNA Nano or Pico assays (Agilent Technologies).

For human heart samples, libraries were constructed using the KAPA RNA Hyper Prep Kit with RiboErase (HMR) (KAPA Biosystems), according to the manufacturer’s instructions. For cultured fibroblasts (CRISPR experiment), libraries were constructed using the KAPA mRNA HyperPrep Kit (KAPA Biosystems), selecting polyA containing mRNA using oligo-dT magnetic beads, according to the manufacturer’s instructions. For cells isolated from the kidney capsule, given the low RNA input, libraries were constructed using the SMARTer Stranded Total RNA-Seq Kit v2-Pico (Takara), according to the manufacturer’s protocol.

All the libraries were checked for quality and concentration using the D5000 ScreenTape assay (Agilent Technologies) and quantitative PCR (KAPA Biosystems), according to the manufacturers’ instructions; pooled and sequenced 75 bp paired-ended (human samples) or single-end (cultured and sorted fibroblasts) on the NextSeq 500 (Illumina).

Single-cell RNA sequencing

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Fibroblasts isolated from the different tissues were FAC sorted and loaded onto a single channel of the 10X Genomics Chromium single-cell platform. Briefly, cells were loaded for capture using the v2 single-cell reagent kit. Following capture and lysis, cDNA was synthesized and amplified (14 cycles) as per manufacturer’s protocol (10X Genomics). The amplified cDNA was used to construct an Illumina sequencing library and sequenced on a single lane of a HiSeq 4000.

Bioinformatics analyses

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For microarray experiments, data extraction and preprocessing were performed as described previously (Furtado et al., 2014b). In brief, raw single-channel signals were extracted (Agilent Feature Extraction Software v.11.0.1.1), and quality control was performed using the default ‘Compromised’ option in (GeneSpring GX v.12.6), with threshold raw signal of 1.0. The approximate mean of 24 samples × ~55,000 probes (10,000) was used as a natural threshold between high- and low-intensity probes. If several probes represented a single gene, the mean of these probes was used. Probes that could not be mapped to any gene were discarded. Log2 transformation and quantile normalization were done using the R package limma v3.48.3. Differential analysis was performed using limma v3.48.3, which fits a linear model to the gene expression data, revealing the differential expression patterns (Benjamini–Hochberg adjusted p value <0.05 and fold change >2). These genes were extracted from the transcriptome to generate a heatmap together with hierarchical clustering dendrograms using MultiExperiment Viewer (MeV) (Saeed et al., 2003). Differentially expressed genes showing more than tenfold change in any given organ were retrieved and an interaction file listing in which organs these genes were enriched was constructed. The interaction file was used as input for Cytoscape (Shannon et al., 2003) in order to reconstruct the network of genes shared by two or more organs, or specifically enriched in only one organ. The network layout was constructed using a Spring Embedded layout and MeV. GO over-representations for the organ-specific subset of genes was performed using the Cytoscape Bingo plug-in. Ingenuity Pathway Analysis was performed using the IPA software (Qiagen).

For single-cell RNA sequencing analyses of freshly isolated fibroblasts, stromal cell data from the Mouse Cell Atlas (Han et al., 2018) were kindly provided by Dr. Guoji Guo and Dr. Huiyu Sun. The data were reanalyzed using Seurat v3 (Stuart et al., 2019). Cells with less than 200 and more than 2500 transcripts were filtered out. Out of the original aggregate, containing 21 samples and 4830 cells, 5 populations of interest were selected for further analysis: ‘Lung’, ‘Testis’, ‘Kidney’, ‘Liver’, and ‘NeonatalHeart’, corresponding to 682 cells. Data were natural-log normalized and scaled using the top-2000 most variable features in the raw data. Principal component analysis (PCA) dimensionality reduction was calculated on 50 principal components; the Uniform Manifold Approximation and Projection (UMAP) dimensional reduction was calculated on 24 dimensions; cluster determination was performed using shared nearest neighbor at a 0.5 resolution. Cluster marker genes were identified with the FindAllMarkers function, using the default Wilcoxon Rank Sum test, at a threshold of 0.25 and a minimum difference in the fraction of detection (min.diff.pct) of 0.3. Pairwise comparisons were done using the FindMarkers function, with MAST assay and only testing genes that are detected in 25% of cells in either of the two populations (min.pct = 0.25).

For bulk RNAseq analysis on cultured fibroblasts post-CRISPR-Cas9 knockdown or kidney capsule implant: Single-end, Illumina-sequenced stranded RNA-Seq reads were filtered and trimmed for quality scores >30 using a custom python script. The filtered reads were aligned to Mus musculus GRCm38 using RSEM (v1.2.12) which performed alignment using Bowtie2 (v2.2.0) (command: rsem-calculate-expression -p 12 --phred33-quals --seed-length 25 --forward-prob 0 --time --output-genome-bam -- bowtie2). RSEM calculated expected counts and transcript per million. The expected counts values from RSEM were used in the edgeR 3.20.9 package to determine differentially expressed genes (based on fold change >1 and FDR < 0.05) (Robinson et al., 2010).

For single-cell RNA sequencing data from cultured fibroblasts, Illumina basecall files (*.bcl) were converted to FASTQ files using Cell Ranger v1.3, using the command-line tool bcl2fastq v2.17.1.14. FASTQ files were then aligned to mm10 genome and transcriptome using the Cell Ranger v1.3 pipeline, which generates a gene versus cell expression matrix. The data were analyzed using Seurat v3 (Stuart et al., 2019) using the same pipeline and parameters as described above, unless stated below. Given the high average number of features, cells with less than 200 and more than 8500 transcripts were filtered out, obtaining 1121 cells. Data were normalized and scaled as described above. PCA dimensionality reduction was calculated on 50 principal components; UMAP dimensional reduction was calculated on 28 dimensions (value chosen based on the ElbowPlot of the standard deviations of the principal components).

qPCR cDNA synthesis of RNAs used for the microarray was performed using the Superscript VILO kit (Invitrogen) following the manufacturer’s instructions. PCR reactions were performed using GoTaq Green master mix (Promega). qPCR reactions were performed using SYBR green master mix (Roche) and analyzed using the LightCycler 480 (Roche). At least two individual experiments in triplicate were performed. We tested several primers for endogenous control (Tbp, Gapdh, L13, Ppi, Actab, and Hprt) and chose Hprt for further experiments due to its consistent reproducibility within and among samples (Figure 1—figure supplement 1). Primers are described in Supplementary file 1. All PCR reactions were performed in triplicates and repeated at least twice per sample. Standard error of the mean is represented in all graphs. Prism v7.0 was used for the generation of graphs and statistics.

Neonatal mouse cardiomyocyte isolation

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The protocol from neonatal cardiomyocyte isolation was adapted from Argentin et al., 1994. Hearts were collected from litters of 1–3 days old pups, cut open and transferred to trypsin (1 mg/ml in HBSS with phenol red pH 6.4) for overnight digestion at 4°C. The next day, hearts were subjected to 3 × 5 min digestions with Collagenase II (1 mg/10 ml; Worthington) in HBSS with phenol red. The cell suspension was collected in DMEM containing 10% fetal calf serum (FCS) and passed through a 100 µm cell strainer. After 5 min centrifugation at 1000 rpm, cells were plated in 10 cm dishes. Two rounds of 1-hr preplating were done to remove cells highly adherent to plastic such as fibroblasts, before seeding the cell suspension on plates coated with 1:200 fibronectin (Thermo Fisher) in 0.1% gelatin (Thermo Fisher).

2D cocultures

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Adult fibroblasts isolated from the heart or kidney of Col1a1-GFP mice were cultured to semiconfluence for 3–5 days, after which they were resuspended and cocultured with mouse neonatal cardiomyocytes at a 4:1 ratio. After 24 hr, media was changed to DMEM containing 2% FCS, videos were recorded, and cells were imaged with an Eclipse Ts2 inverted fluorescence microscope (Zeiss) and fixed with 4% PFA for 10 min at 4oC for further staining.

Cardiac microtissues

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Cardiac microtissues were generated as previously described (Boudou et al., 2012; Hinson et al., 2015), using polydimethylsiloxane (PDMS) 3D microarrays with 24 microwells containing cantilevers. A suspension of 1.3 million cells, 85% hiPSCs derived cardiomyocytes (iCM) and 15% cardiac or kidney fibroblasts, was loaded on each device and cells were seeded in each well by centrifugation. Two millitissue devices with 48 organoids were used per fibroblasts type. The organoids were imaged and fixed 3 days postproduction with 4% PFA for 15 min at RT. Only tissues uniformly anchored to the tips of the cantilevers were included in further analysis.

Immunostaining

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A solution containing 2% bovine serum albumin (BSA), 2% FCS, 0.1% Triton in phosphate buffered saline (PBS) was used for permeabilization, blocking, and dilution. Primary antibodies used in this study are: KRT14 (MA5-11599, Thermo Fisher, mouse monoclonal, 1:100), TBX20 (MAB8124, Novus Biologicals, mouse monoclonal, 1:200), FOXA2 (ab108422, Abcam, rabbit monoclonal, 1:300), HHEX (MAB83771, R&D System, rabbit monoclonal, 1:100), FOXD1 (TA322737, OriGene, rabbit polyclonal, 1:50), PAX8 (NBP2-29903, Novus Biological, mouse monoclonal, 1:100), RSPO1 (AF3474, R&D Novus, goat polyclonal, 1:50), and TNT (RC-C2, DSHB 1:200). Cells were stained overnight at 4oC, washed in PBS and incubated 1 hr with 1:500 secondary antibodies (Alexa Donkey anti Goat Alexa Fluor 568 A11057, Goat anti Mouse Alexa Fluor 568 A11031, Goat anti Rabbit Alexa Fluor 555 A27017; Thermo Fisher). Nuclei were counterstained with 0.1 µg/ml DAPI (D1306, Thermo Fisher).

For the immunostaining of cardiac microtissues, blocking and permeabilization were achieved with 0.1% Triton, 1% BSA in PBS (PBS-T-BSA) for 8 hr. The same solution was used to dilute primary antibodies: TNT (RC-C2, DSHB 1:200) or MF20 (DSHB, mouse monoclonal, 1:100). Staining was performed overnight under gentle agitation at 4oC. After 3 × 5 min washes in PBS-T-BSA, microtissues were stained with secondary antibody and DAPI for 1 hr at room temperature. After staining, the PDMS devices were pulled out of the 35 mm dish used as support and flipped on glass coverslip for confocal imaging.

Imaging

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Immunofluorescence images were acquired using either the either the upright fluorescent microscope Axio Imager.Z2 (Zeiss) or the SP8 confocal microscope equipped with a White Light Laser (Leica). For the cardiac microtissues, 3–4 tiles and 20–34 Z-stacks were imaged per sample. Tiles were combined using the LeicaX confocal software. Z-stack projections and analysis were performed using Fiji version 1.0 and Imaris 8.4.1.

Cell transfection and crispr knock-down

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Cardiac fibroblasts from ROSACas9-EGFP mice were transfected with guide RNAs using Lipofectamine MessengerMAX (Thermo Fisher) according to the manufacturer’s instructions. Briefly, 3 days postisolation, CD45−CD31−CD90 + cells were FACS sorted and replated at about 10,000 cells/cm2. After 6 days, when reaching 80–90% confluency, cells were incubated for 5 min with the RNA (1:50)–lipid (1:33) complex in Opti-MEM for 10 min at room temperature. Media was changed after 48 hr and cells were collected for RNA isolation at 72 hr. Two guide RNAs, designed and synthesized in-house (JAX Genetic Engineering Technologies facility), were used for each of the target genes (Figure 5—source data 1). CleanCap mCherry mRNA (TriLink Biotechnologies), and guide RNAs for GFP and scrambled guides were used as controls. Guide RNA for the GFP gene were same as in Li et al., 2014.

Cell transplantation in the kidney capsule

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Adult fibroblasts from heart, tail, and kidney were isolated from 10-week-old ROSA mT/mG male mice as described above. After 10 days cells were collected, counted and 4–5 × 105 cells were transferred to individual 1.5 ml Eppendorf tubes (one per each kidney transplant), resuspended in 15–20 µl of saline solution and kept on ice until surgery. Remaining cells were replated (5 × 104/well; 6-well plate) for cultured cell controls. Syngeneic 10–11 weeks old C57BL6/J mice were used as cell recipients. Mice were anesthetized with 400 mg/kg tribromoethanol intraperitoneally. In parallel, cell suspensions were spotted on a Petri dish and fragments of sterile absorbable gelatin foam (about 1 mm long; Surgifoam, Ethicon) were immersed in the drop.

Fur was removed from the left flank of the animal and eye ointment was applied. The mouse was placed in right lateral recumbency, and a drape positioned over the surgical site. A 6–9 mm skin incision was made parallel and ventral to the spine and midway between the last rib and the iliac crest. A similar incision was made in the underlying abdominal wall. The kidney was externalized by placing forceps under the caudal pole and gently lifting through the incision, kept moist with warm sterile saline. A small incision was made in the capsule over the caudal–lateral aspect of the kidney, and a shallow subcapsular pocket was made with a blunt probe advanced toward the cranial pole of the kidney. The foam previously soaked in the cell suspension was placed in the far end of the subcapsular pocket. If needed, additional foam was used to close the incision site. Absorbable 6.0 Vicryl sutures (Ethicon) were used to close the abdominal wall, 6.0 Vicryl black sutures (Ethicon) for the skin. 0.1% Bupivacaine was applied topically on the injection site and 0.05 mg/kg SR buprenorphine (Zoopharm) was injected subcutaneously.

Data availability

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Sequencing data have been deposited in GEO under accession codes GSE98783 for the microarray experiment, and GSE175765 for the bulk RNAseq datasets generated for the CRISPR experiment, kidney capsule transplant experiment and human cardiac biopsies comparison. Single-cell RNA sequencing data of primary cultured fibroblasts have been deposited in SRA with the identifier SRR5590304. The scRNAseq stromal cell dataset from the Mouse Cell Atlas (Figure 6 in Han et al., 2018) was kindly provided by Dr. Guoji Guo’s lab. Source Data files have been provided for Figures 1, 2, 4 and 5. The code used for the manuscript is currently located at https://github.com/Ramialison-Lab-ARMI/MultiFibroblasts, (Forte, 2021 copy archived at swh:1:rev:89df5f3cdc503ee4fe603ea956d935b48dc6b669).

Data availability

All data have been made available through public databases, as per statement in main manuscript.

The following data sets were generated
    1. Ramialison M
    2. Nim HT
    3. Furtado MB
    (2017) NCBI Gene Expression Omnibus
    ID GSE98783. Transcriptional profile of organ fibroblasts from adult mice.
    1. Nim HT
    (2021) NCBI Gene Expression Omnibus
    ID GSE175765. RNA-sequencing transcriptional profile of kidney capsule derived organ fibroblasts from adult mice, CRISPR-knockdown organ fibroblasts from adult mice, and human hearts.
    1. Jackson Laboratory
    (2017) NCBI Sequence Read Archive
    ID SRR5590304. Tissue fibroblasts conserve an organ molecular identity.
    1. Han X
    2. Wang R
    (2018) NCBI Gene Expression Omnibus
    ID GSE108097. Mapping Mouse Cell Atlas by Microwell seq.

References

    1. Lei L
    2. Zhao C
    3. Qin F
    4. He Z-Y
    5. Wang X
    6. Zhong X-N
    (2016)
    Th17 cells and IL-17 promote the skin and lung inflammation and fibrosis process in a bleomycin-induced murine model of systemic sclerosis
    Clinical and Experimental Rheumatology 34 Suppl 100:14–22.

Decision letter

  1. Paul W Noble
    Senior and Reviewing Editor; Cedars-Sinai Medical Center, United States

Our editorial process produces two outputs: i) public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Adult fibroblasts retain organ-specific transcriptomic identity" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Paul Noble as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

Reviewer #1 (Recommendations for the authors):

The manuscript has strengths in: Figure 7 transplant data were strong; The authors have provided extensive data; Functional confirmation in cardiac lineage was very convincing. The recommendations are:

1. Verification and gene list. A weakness in the initial gene analysis is there are so many genes and pathways mentioned and used (Figure 2a) that it is difficult to determine why the genes in Figure 2 (b-g) were the ones chosen to be validated. The qPCR validation seems to support the hypothesis that these genes have organ specific expressions but their selection (top gene? Specific pathway node? Candidate gene?) from the initial analysis is unclear. It would help to simplify the schematic for Figure 2a and highlight the specific genes that are being validated. This is further compounded by the figure 3 data, which shows mixed results (PAX8 does not really seem to be expressed in the kidney and FOXD1 seems to have an odd pattern of expression; FOXA2 seems to be expressed in some nuclei and not others of the lung) and non-direct comparison between multiple organs.

2. The clusters of the scRNA-seq from both freshly isolated and cultured fibroblasts seem to be due to the batch effects, as it is not very possible that not a single overlapped cell was identified. The listed organ specific genes in heatmaps were hand-picked? as they are identical. Are there any specific genes between fresh isolated and cultured fibroblasts in each organ? A better suggestion should be listing all the shared maker genes and organ specific genes in both freshly isolated and cultured fibroblasts and discussing a little bit the possible related functions.

3. Immunocytochemistry validation should also include the staining on the negative fibroblasts to confirm the "organ specific markers" in Figure 3. More convincing staining experiments should be on the sections of freshly isolated organs with proper and necessary controls.

Reviewer #2 (Recommendations for the authors):

The reviewer has some comments on the data presentation, analysis and overall experimental approach that are listed below:

Data presentation:

1) Figure 6i: -LOG(p-value) should be underneath of the X-axis. It does not make sense to show pathways that are not significant, i.e. -LOG(p-value) < 1.3.

2) Figure 5 —figure supplement 1 and Figure supplement 2 are confusing. Figure5 – S1 shows mainly lung, and Figure 5 – S2 shows heart, and it is not clear why both figures also have some data on the kidney.

Analysis:

1) The authors have added single-cell profiling of fibroblasts from the published mouse dataset (Han et al. 2018). However, it would have been more informative to include instead published datasets on human fetal and adult tissues, as these datasets for the heart, kidney and lung are available.

2) Why was microarray and not RNA-seq has chosen to perform gene expression analysis? It is much better to have RNA-seq data instead of as it makes datasets easier to compare with other published datasets.

Experimental approach:

1) The authors used cultured fibroblasts to avoid contamination from parenchyma cells. However, this approach is not ideal, especially because the authors report that cultured cells present an activated/myofibroblast-like phenotype compared to freshly isolated cells. It might also be helpful to describe more in-depth the activated phenotype upon culture compared to freshly isolated cells. Are these get downregulated upon ectopic transplantation?

2) Tissue-specific functionality of fibroblasts in vitro (3D cardiac tissue function) and in vivo (ectopic transplantation) are performed after just 3 days, which might be not sufficient to reveal more differences or similarities. It might be interesting to have these compared to a prolonged period upon co-culture or transplantation, such as three weeks. The reviewer also finds it surprising that the authors observe the change in HOX genes expression as early as 3 days after transplantation, as one would think this should take longer.

Reviewer #3 (Recommendations for the authors):

The authors provide a well-written and comprehensive study of the positional "memory" and specialization of fibroblasts. As such, I have only minor comments. Please find below my part-by-part comments to the manuscript.

Abstract

The final sentence of the abstract does not fit with the narrative of the paper, it is too much of a future perspective.

Discussion

Buechler et al. (Nature, 2021) defined in their fibroblast atlas two universal fibroblast populations, from which tissue-specific fibroblasts appear to be derived. The present manuscript does not provide such a cell type: rather than a continuum of states, discrete organ-specific cell states are found. The authors should comment on this discrepancy. Furthermore, the discussion should integrate the present findings with those of Buechler, who touches upon the concept of spatial fibroblast heterogeneity, making the manuscript a direct extension of his findings.

Line 409 seems like an overstatement, as the concept of fibroblasts as a cell type is not at stake here. Rather, the definition of cell state might require an amendment, taking into consideration anatomical location or the local microenvironment to which a cell is exposed.

Figures

The added value of Figure 1 is lost to me. It does not provide much information and would in my view better be suited for the supplements. Regarding Figure 1, lines 108 to 110 can be removed. The statement that is it surprising for a cell type designated mainly as ECM-producing to be rich in transcripts for nuclear and cytosolar proteins is confusing and should therefore be omitted. A cell's primary function does not mean that transcripts related to that function should by default make up the majority of the transcriptome.

Why are averaged raw signals used in Figure 2, rather than normalized expressions?

Figure 5 contains two clusters which cannot be linked back to the organ of origin. Perhaps regressing out the genes of the Hox cluster and the cell cycle can solve this issue?

Figure 6j is missing the cardiomyocyte signature in the Tbx20 KD, while it is the positive control.

Figure 8c does not have a legend. In 8d it would help to move the word 'FDR' next to the legend's color scale.

Results

The first subtitle mentions metabolic components. As these were part of larger gene sets, such as housekeeping, the cytoskeleton, proliferation, this word should be substituted by e.g. homeostatic. No metabolic profiling was performed.

The description of population "KidneyA" in lines 217 to 221 aims to explain a relatively higher expression of genes related to in the response to injury, linking this to tubular cells acquiring mesenchymal phenotypes in vivo. Considering the relative size of the cluster, this seems unlikely to me. It would be of interest to generate a module score for the genes associated to the collagenase-response described by O'Flanagan et al. (Genome Biology, 2019) and test if this cluster is not simply a group of fibroblasts with a stronger reaction to the enzymatic digestion. This can be done easily in Seurat v3 using the AddModuleScore() function.

Line 276: the use of the abbreviation 'HF' for heart failure is not defined. Considering that the abbreviation eHF is coined a few lines earlier to refer to heart fibroblasts, this leads to possible confusion.

Materials and Methods

Lines 493 and 610 mention 10cm dishes, which I believe should be 10 cm² dishes.

Lines 488 and 607 do not mention the basal medium in which the digestion took place.

Line 491 does not state the percentages of sodium pyruvate and pen/strep in the medium.

Line 493 contains a typo in the TrypLE.

Line 541: "log2 transformation and quantile normalization was done using R scripts and public Bioconductor packages". This is too vague to be reproducible, please elaborate and cite the packages used for the analysis.

Line 578: a citation of edgeR is missing (Robinson, Bioinformatics, 2010).

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Adult mouse fibroblasts retain organ-specific transcriptomic identity" for further consideration by eLife. Your revised article has been evaluated by Paul Noble (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

Please address the batch effect issue as outlined by reviewer #3 as it impacts the significance of the findings.

Reviewer #1 (Recommendations for the authors):

The new version of manuscript has satisfactorily answered my concerns.

Reviewer #2 (Recommendations for the authors):

The authors have addressed all my comments, and I have no additional comments.

Reviewer #3 (Recommendations for the authors):

The authors have adequately addressed most of my comments in both the revisions and their responses. One issue which does persist in my opinion, and which is also mentioned by Reviewer 1 is the grouping of the cells in the scRNA-seq analysis. From a biological perspective at least some overlap would be expected in an analysis only including one cell type. However, there is a very strong separation based on organ, highly resembling a batch effect. In their response to Reviewer 1, the authors mention this could not be possible as all samples were sequenced in the same 10x lane. I believe that by this they mean the same 10x chip, as running 8 samples in the same lane would require additional multiplexing. Regardless, even within the same chip, some technical variation can be expected between lanes.

On this topic the authors also refer to figure 6 of the Mouse Cell Atlas (MCA) paper, but this does not make sense as different dimensionality reduction methods were used between these two papers. The MCA uses tSNE projection, which prioritizes finding local communities, while in this paper UMAP was used, which focuses preserving the global structure of the data. A side effect of the mathematical differences between these methods is that intercluster distance does not have much meaning in tSNE space, but reflects variation in a UMAP projection. Consequently, separation by organ in a UMAP projection as seen here does indeed support the claim of the manuscript. However, the strong separation of samples derived from the same organ (e.g. the two kidney samples) undermines this reasoning, as it implies the presence of a batch effect aiding in the separation. This is not sufficiently addressed by the authors.

– The caption of Figure 2 says b-g, while this should be b,c.

– Figure 7 is labelled a-i, l-m. The panel letters should be updated.

https://doi.org/10.7554/eLife.71008.sa1

Author response

Essential revisions:

Reviewer #1 (Recommendations for the authors):

1. Verification and gene list. A weakness in the initial gene analysis is there are so many genes and pathways mentioned and used (Figure 2a) that it is difficult to determine why the genes in Figure 2 (b-g) were the ones chosen to be validated. The qPCR validation seems to support the hypothesis that these genes have organ specific expressions but their selection (top gene? Specific pathway node? Candidate gene?) from the initial analysis is unclear. It would help to simplify the schematic for Figure 2a and highlight the specific genes that are being validated. This is further compounded by the figure 3 data, which shows mixed results (PAX8 does not really seem to be expressed in the kidney and FOXD1 seems to have an odd pattern of expression; FOXA2 seems to be expressed in some nuclei and not others of the lung) and non-direct comparison between multiple organs.

After establishing that the fibroblasts retain a positional identity as shown by the distinct Hox genes expression patterns (Figure 1), we performed differential expression analysis and considered genes that were enriched by 10-fold or more, in single organ fibroblasts compared to tail fibroblasts. Gene Ontology annotation of these organ-enriched genes revealed terms associated with organ development programs; genes in those development related GO terms are shown in the Cytoscape plot (Figure 2 of the re-submission); we have clarified this point in the figure captions (Figure 2, 3) and text. The expression of developmental genes was validated by qPCR on bulk RNA extracted from sorted primary cultured adult fibroblasts and from embryonic fibroblasts (E16.5).

In Figure 3 we validated the expression of the same genes at the protein level using immunocytochemistry. As the reviewer rightly pointed out, the expression of markers like Pax8 and Foxa2 seem to be restricted to certain fibroblasts subtypes (i.e., Pax8 expression is mostly observed in GFP low kidney fibroblasts; Foxa2 nuclear expression seems restricted to smaller lung fibroblasts, not observed in myofibroblast-like cells), showing that despite the selection for CD45- CD31- CD90+ cells, we are still working with populations of fibroblasts in different states. Therefore, we confirmed the expression of the organ-enriched gene signature in scRNAseq data from freshly isolated fibroblasts.

2. The clusters of the scRNA-seq from both freshly isolated and cultured fibroblasts seem to be due to the batch effects, as it is not very possible that not a single overlapped cell was identified. The listed organ specific genes in heatmaps were hand-picked? as they are identical. Are there any specific genes between fresh isolated and cultured fibroblasts in each organ? A better suggestion should be listing all the shared maker genes and organ specific genes in both freshly isolated and cultured fibroblasts and discussing a little bit the possible related functions.

The listed organ-specific genes were predefined based on the signature obtained in the prior network analysis; hence they are identical between the two queried scRNA-seq datasets. The rationale behind the choice is that the core genes preserved under culture conditions are most likely the strongest molecular signature for the cell type. Therefore, we kept looking for this signature in further experiments.

We can exclude a significant batch effect, as the freshly isolated cell data (same dataset subset used in Figure 6 of Mouse Cell Atlas papers) and cultured fibroblasts data (generated by this paper, using fibroblasts cultured under the same conditions and captured in one lane of the 10xChromium) were analyzed separately, and no data integration was performed. Performing data integration would indeed cause batch effects and the resulting bias would outweigh the limited additional insights.

Genes shown in Figure 5b/5e are shared between freshly isolated and cultured fibroblasts. We have provided the relevant function of these organ specific genes (Figure 3—figure supplement 2-7).

3. Immunocytochemistry validation should also include the staining on the negative fibroblasts to confirm the "organ specific markers" in Figure 3. More convincing staining experiments should be on the sections of freshly isolated organs with proper and necessary controls.

We thank the reviewer for this comment. We have not included negative controls in the figure previously, but all controls were performed for every experiment. We have now added the negative staining images (secondary antibody only) for the immunocytochemistry experiments on primary Col1a1-GFP+ fibroblasts (Figure 3—figure supplement 1).

The rationale behind performing the staining on primary isolated cells is that parenchymal cell staining can confound visualization of results. For example, Tbx20 is also expressed by other cell types of the heart, including cardiomyocytes and endothelial cells. Performing staining in isolated fibroblasts, clearly displays the overlap between GFP+ fibroblasts and the desired antibody. We are confident the stainings are not artifactual as: 1. primary antibodies show expected expression patterns; 2. negative controls are clean and 3. the protein expression data provided here matches our single cell RNA data analyses.

Reviewer #2 (Recommendations for the authors):

The reviewer has some comments on the data presentation, analysis and overall experimental approach that are listed below:

Data presentation:

1) Figure 6i: -LOG(p-value) should be underneath of the X-axis. It does not make sense to show pathways that are not significant, i.e. -LOG(p-value) < 1.3.

This has been fixed.

2) Figure 5 —figure supplement 1 and Figure supplement 2 are confusing. Figure5 – S1 shows mainly lung, and Figure 5 – S2 shows heart, and it is not clear why both figures also have some data on the kidney.

Figure 5 is now Figure 4.

Supplement 1 and 2 include heatmaps to highlight the differential gene expression among the subclusters of fibroblasts from the same organ. From the freshly isolated stromal cells aggregate (Mouse Cell Atlas 2018), only lung and kidney showed multiple sub-clusters, which are further explored in Supplement 1 (Figure 4 —figure supplement 1). From the in-house scRNAseq dataset on combined cultured fibroblasts, only heart and kidney showed multiple subclusters (Figure 4 —figure supplement 2).

Please note that the analyses are unbiased, so the number of sub-clusters found per organ has not been pre-defined by us. For both studies (fresh isolated cells re-analyzed and cultured cells run in house) we input cells from various organs (kidney, lung, heart, etc).

Analysis:

1) The authors have added single-cell profiling of fibroblasts from the published mouse dataset (Han et al. 2018). However, it would have been more informative to include instead published datasets on human fetal and adult tissues, as these datasets for the heart, kidney and lung are available.

The original idea of the manuscript is to demonstrate a core molecular signature kept to adulthood. Embryonic analysis is complicated since organ fibroblasts are only nascent at late fetal time-points.

However, to satisfy the reviewer’s request we have now performed additional analyses of two longitudinal datasets in the mouse:

a. lung E16.5 and E17.5 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE156329, https://doi.org/10.1016/j.isci.2021.102551)

b. kidney E15.5, E17.5, P0 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE149134, https://doi.org/10.1016/j.ydbio.2020.11.002).

We analyzed these datasets separately without data integration, due to the different samples and sequencing conditions. The lung data confirmed the presence of our adult lung fibroblast signature (e.g. Foxf1, Bmp3; left panel of Author response image 1) in the Col1a2+;Vim+ subpopulation of embryonic lung fibroblast (~5500 cells). Thy1 was not significantly expressed at this stage in lung cells, therefore this marker was excluded from the analysis. The time-course expression further revealed the dynamic change of this signature over two developmental time-points. Confirmation was obtained using Module Score analysis (right panel; ‘lung embryonic fibroblasts’ violin plots, generated by the AddModuleScore() function in Seurat v3 with default parameters), where the lung fibroblast signature was above the expression of n=100 randomly selected control genes (dashed line) for a subpopulation of lung fibroblasts at E16.5 and E17.5. The module score of E17.5 fibroblasts (3 replicates in different colors – green, cyan and purple) is higher than that of E16.5 fibroblasts (red), suggesting cell maturation plays a role in cementing the adult core molecular code.

Author response image 1

Similarly, the embryonic longitudinal kidney data for Thy1+, Col1a2+, Vim+ (~1000 cells) confirmed the adult kidney fibroblast signature was present in embryonic fibroblasts (e.g. Pax8; left panel in Author response image 2). Similar to the embryonic lung fibroblast data, the aggregated expression of the kidney fibroblast signature was above the expression of n=100 randomly selected control genes (dashed line) for a subpopulation of kidney fibroblasts at E15.5 and E17.5 (right panel; ‘kidney fibroblasts’ module score violin plots, generated by the AddModuleScore() function in Seurat v3 with default parameters). Once again, the module score of E17.5 is higher than that of E15.5.

Author response image 2

2) Why was microarray and not RNA-seq has chosen to perform gene expression analysis? It is much better to have RNA-seq data instead of as it makes datasets easier to compare with other published datasets.

The reason is historical. The microarray data has been performed many years ago and is publicly available but remains unpublished. At the time, microarray technology in mice was mature and high-quality probes were fully established while the RNA-seq technology was emerging. The generated data were robust for cross-tissue interrogation, network analysis, and Hox code analysis. We rationalized that the original bulk microarray data was high quality and we deemed unnecessary to repeat it using RNA-seq posteriorly. However, all data further generated for this manuscript has utilized RNA-seq.

Experimental approach:

1) The authors used cultured fibroblasts to avoid contamination from parenchyma cells. However, this approach is not ideal, especially because the authors report that cultured cells present an activated/myofibroblast-like phenotype compared to freshly isolated cells. It might also be helpful to describe more in-depth the activated phenotype upon culture compared to freshly isolated cells. Are these get downregulated upon ectopic transplantation?

Myofibroblasts and their markers are very well characterized in the literature1,2. Based on the GEO terms found in this work and previous literature, cells in culture tend to heterogeneously acquire a myofibroblast phenotype due to growth factors in the serum, including TGFB3. On the contrary, freshly isolated cells show a homeostatic phenotype, Acta2 negative4.

From the single-cell data analyses of both freshly isolated fibroblasts (Figure 4a-c, Figure 4-Source Data 1, Figure Supplement 1 in the revised version) and primary cells in culture (Figure 4d-f, Figure 4-Source Data 2, Figure Supplement 2 in the revised version), we noticed that, when multiple subtypes were present, the signature genes identified from the microarray analysis of the CD45-CD31- Thy1+ fibroblasts tended to be relatively more expressed in the subpopulation with an activated/myofibroblast phenotype. It is interesting that transplanted cells did show increase in GEO terms related to myofibroblast activation, inflammation and fibrosis (Figure 7—figure supplement 1). Our interpretation is that transplanted cells may see the kidney capsule as a foreign/insult environment.

2) Tissue-specific functionality of fibroblasts in vitro (3D cardiac tissue function) and in vivo (ectopic transplantation) are performed after just 3 days, which might be not sufficient to reveal more differences or similarities. It might be interesting to have these compared to a prolonged period upon co-culture or transplantation, such as three weeks. The reviewer also finds it surprising that the authors observe the change in HOX genes expression as early as 3 days after transplantation, as one would think this should take longer.

The reviewer raises a good point. We thought to analyze the transplanted cells after a short time because those were primary fibroblasts transplanted in syngeneic (non-immunocompromised mice), not pluripotent cells as in the classic teratoma assays5. In the abovementioned study, luminescence from Fluor-tagged embryonic stem cells disappeared by one week and reappeared 2-7 weeks after transplantation, suggesting the selection of a few resistant clones. In our case, the short transplantation period reduced the chance of sub-selection of transformed cells and the risk of fusion with recipient cells.

We have not tried to keep them for longer periods, and unfortunately, we are not able to perform additional wet-lab experiments at this stage.

Reviewer #3 (Recommendations for the authors):

Abstract

The final sentence of the abstract does not fit with the narrative of the paper, it is too much of a future perspective.

The final sentence has been revised accordingly and now reads as:

“In conclusion, our data reveal that adult fibroblasts maintain an embryonic gene expression signature inherited from their organ of origin, thereby increasing our understanding of adult fibroblast heterogeneity. The knowledge of this tissue-specific gene signature may assist in targeting fibrotic diseases in a more precise, organ-specific manner.”

Discussion

Buechler et al. (Nature, 2021) defined in their fibroblast atlas two universal fibroblast populations, from which tissue-specific fibroblasts appear to be derived. The present manuscript does not provide such a cell type: rather than a continuum of states, discrete organ-specific cell states are found. The authors should comment on this discrepancy. Furthermore, the discussion should integrate the present findings with those of Buechler, who touches upon the concept of spatial fibroblast heterogeneity, making the manuscript a direct extension of his findings.

We have modified the discussion to address this issue, lines 457 to 464.

Buecheler et al.6 describe a common fibroblast hierarchy across different tissues, with two universal fibroblasts populations, characterized by distinct spatial localization (adventitial versus parenchymal), generating activated or specialized fibroblasts. However, as they mention in the discussion “It is possible that fibroblast subsets may exhibit additional imprinting by their tissue of residence”. We believe our data do not contradict Buecheler et al.’s findings, but the nature our initial analysis does not allow us to explore the full fibroblasts state continuum. Our aim is to identify tissue-specific fibroblast signature genes, as opposed to a generic signature, although such genes were incorporated into the original Figure 1 (now Figure 1—figure supplement 1b-c; Figure 1-Source Data 1).

However, to answer the point raised by the reviewer, we have generated module scores for each of our tissue-enriched signature gene list and enquired whether these expression signatures can be preferentially enriched in any of the two steady-state pan-tissue fibroblast subpopulations described by the Buecheler et al. dataset (violin plots below). We did not observe any common pattern across the different tissues. In general, gene expression was relatively similar among all clusters, with the exception of gonad and heart, that show a tendency toward a relatively higher expression in the parenchymal cluster, and skin with a relatively higher expression in the adventitial cluster.

Author response image 3

Line 409 seems like an overstatement, as the concept of fibroblasts as a cell type is not at stake here. Rather, the definition of cell state might require an amendment, taking into consideration anatomical location or the local microenvironment to which a cell is exposed.

We have toned down this statement as suggested by the reviewer to “suggesting a strong influence of the anatomical location or the local microenvironment on the cell state.” (now lines 456-457).

Figures

The added value of Figure 1 is lost to me. It does not provide much information and would in my view better be suited for the supplements. Regarding Figure 1, lines 108 to 110 can be removed. The statement that is it surprising for a cell type designated mainly as ECM-producing to be rich in transcripts for nuclear and cytosolar proteins is confusing and should therefore be omitted. A cell's primary function does not mean that transcripts related to that function should by default make up the majority of the transcriptome.

We have edited the figures and manuscript accordingly: Figure 1 has been moved to Figure 1—figure supplement 1b-c. We have removed the last part of paragraph 1 referring to the cellular localization of commonly expressed genes.

Why are averaged raw signals used in Figure 2, rather than normalized expressions?

Figure 2 is now Figure 1. The idea behind using raw signal instead of normalization is that tail can also be a queried sample in this particular figure. This way we could present all tissues in separate plots. Comparisons were only made between the expression of Hox gene members within the same organ, which could be made based on absolute expression levels. In this case, normalization was not required and would not change the ranking between Hox genes within the same tissue.

Figure 5 contains two clusters which cannot be linked back to the organ of origin. Perhaps regressing out the genes of the Hox cluster and the cell cycle can solve this issue?

We believe those clusters are likely due to non-fibroblasts populations, because the single-cell analysis was done on primary cells (cultured for 5-days) sorting only based on the viability markers not on CD45-CD31-Thy1+, as in the initial bulk microarray analyses.

We have already regressed out the cell cycle genes in the presented analysis, but we thought regressing out the Hox genes (very strong in those non-fibroblasts populations) would alter our clustering.

The Hox code signature is very strong in these clusters; regressing Hox genes would dilute all signatures in all clusters. Although we can’t explain what these cells are, we kept them in the plot for transparency.

Figure 6j is missing the cardiomyocyte signature in the Tbx20 KD, while it is the positive control.

The ‘cardiomyocyte signature genes’ we not the positive control. These genes are found in cardiac fibroblasts, albeit at much lower levels than what would be expected for a cardiomyocyte (we estimate ~100 fold or so lower). We have routinely encountered sarcomeric cardiomyocyte genes in cardiac fibroblasts, except for the bona-fide marker myosin heavy chain. We believe that the core transcriptional signature in cardiac fibroblasts generates some spurious low level expression of sarcomeric genes in these cells, although it seems this expression has no biological relevance, considering that cardiac fibroblasts have no sarcomere structures. This reinforces the need to use isolated cultured cells to determine the core transcriptional identity, as carry over RNA could certainly impact such findings.

For the plots in 6j we selected representative genes for each category that were significantly regulated in the KD. Cardiomyocyte signature genes (Tnnt2, Tnnc1) were differentially regulated in the Gata4KD but were not differentially expressed in the Tbx20KD.

Figure 8c does not have a legend. In 8d it would help to move the word 'FDR' next to the legend's color scale.

(c) was not in brackets hence it appeared as if 8c did not have a legend. We have corrected this and the corresponding legend is: “Multidimensional scaling plot calculated on the top 500 genes post-normalization, to visualize the level of transcriptomic similarity among all the samples.” Additionally, we have moved FDR near the legend scale.

Results

The first subtitle mentions metabolic components. As these were part of larger gene sets, such as housekeeping, the cytoskeleton, proliferation, this word should be substituted by e.g. homeostatic. No metabolic profiling was performed.

The first subtitle has been changed to “Cell homeostasis and extracellular matrix components comprise a generic fibroblast gene signature”.

The description of population "KidneyA" in lines 217 to 221 aims to explain a relatively higher expression of genes related to in the response to injury, linking this to tubular cells acquiring mesenchymal phenotypes in vivo. Considering the relative size of the cluster, this seems unlikely to me. It would be of interest to generate a module score for the genes associated to the collagenase-response described by O'Flanagan et al. (Genome Biology, 2019) and test if this cluster is not simply a group of fibroblasts with a stronger reaction to the enzymatic digestion. This can be done easily in Seurat v3 using the AddModuleScore() function.

We have performed the ‘Module Score’ analysis as per the reviewer’s suggestion. We used the kidney marker genes, as well as the collagenase-response genes (“core 512 genes”), as described in O'Flanagan et al.7, to assess the module score based on the Mouse Cell Atlas single-cell data (see panels A, B, C in violin plots in Author response image 4). The data showed that the “KidneyA” population (Panel A) was distinct from the “Kidney B” population (Panel B), based on each corresponding subpopulation marker identified in this study. The same kidney cells however did not show any difference in module scores when using the collagenase-response genes (Panel C), showing that the Kidney A and B sub-populations are not the result of differential reaction to enzymatic digestion. Thus, we maintain our hypothesis that the “KidneyA” cluster corresponds to tubular cells acquiring mesenchymal phenotypes.

Author response image 4

Line 276: the use of the abbreviation 'HF' for heart failure is not defined. Considering that the abbreviation eHF is coined a few lines earlier to refer to heart fibroblasts, this leads to possible confusion.

To avoid confusion, HF was replaced with heart failure on line 276.

Materials and Methods

Lines 493 and 610 mention 10cm dishes, which I believe should be 10 cm² dishes.

Thanks for pointing this out. This has been corrected.

Lines 488 and 607 do not mention the basal medium in which the digestion took place.

We modified the text to explain that the digestion occurred in Trypsin (line 488) or in Collagenase resuspended in HBSS with phenol red (line 607).

Line 491 does not state the percentages of sodium pyruvate and pen/strep in the medium.

We have added the percentages of sodium pyruvate and pen/strep; we have also added GlutaMAX, which was missing under media composition.

Line 493 contains a typo in the TrypLE.

This has been corrected.

Line 541: "log2 transformation and quantile normalization was done using R scripts and public Bioconductor packages". This is too vague to be reproducible, please elaborate and cite the packages used for the analysis.

The text has been modified to incorporate more methodology details. We also provide all coding used for the manuscript under https://github.com/Ramialison-Lab-ARMI/MultiFibroblasts.

Line 578: a citation of edgeR is missing (Robinson, Bioinformatics, 2010).

This reference has been added to the modified manuscript.

References:

1. Baum, J.B.S., Duffy, H.S. Fibroblasts and Myofibroblasts: What Are We Talking About?, Journal of Cardiovascular Pharmacology (2011). DOI: 10.1097/FJC.0b013e3182116e39.

2. Tarbit, E., Singh, I., Peart, J.N. and Rose'Meyer, R.B. Biomarkers for the identification of cardiac fibroblast and myofibroblast cells. Heart Fail Rev (2019). DOI: 10.1007/s10741-018-9720-1.

3. Siani, A., Khaw, R., Manley, O. et al. Fibronectin localization and fibrillization are affected by the presence of serum in culture media. Sci Rep (2015). DOI: https://doi.org/10.1038/srep09278.

4.Forte, E., Skelly, D.A., Chen, M., et al. Dynamic interstitial cell response during myocardial infarction predicts resilience to rupture in genetically diverse mice. Cell Reports (2020). DOI: https://doi.org/10.1016/j.celrep.2020.02.008

5. Zhu, F.; Sun, B., Wen, Y., et al. Modified Method for Implantation of Pluripotent Stem Cells Under the Rodent Kidney Capsule. (2014). DOI: https://doi.org/10.1089/scd.2014.0099

6. Buechler, M.B., Pradhan, R.N., Krishnamurty, A.T., et al. Cross-tissue organization of the fibroblast lineage (2020). DOI: https://doi.org/10.1038/s41586-021-03549-5

7. O’Flanagan, C.H., Campbell, K.R., Zhang, A.W. et al. Dissociation of solid tumor tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses. Genome Biol (2019). DOI: https://doi.org/10.1186/s13059-019-1830-0.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Reviewer #3 (Recommendations for the authors):

The authors have adequately addressed most of my comments in both the revisions and their responses. One issue which does persist in my opinion, and which is also mentioned by Reviewer 1 is the grouping of the cells in the scRNA-seq analysis. From a biological perspective at least some overlap would be expected in an analysis only including one cell type. However, there is a very strong separation based on organ, highly resembling a batch effect. In their response to Reviewer 1, the authors mention this could not be possible as all samples were sequenced in the same 10x lane. I believe that by this they mean the same 10x chip, as running 8 samples in the same lane would require additional multiplexing. Regardless, even within the same chip, some technical variation can be expected between lanes.

We thank the reviewer for their comment and the opportunity to clarify this further. The primary cultured fibroblasts from different organs were mixed in one single lane of the 10x Chromium chip, without multiplexing. We identified the clusters based on the expression of the organ-specific core genes identified from the initial bulk microarray experiment and validated via ICC, and qPCR.

On this topic the authors also refer to figure 6 of the Mouse Cell Atlas (MCA) paper, but this does not make sense as different dimensionality reduction methods were used between these two papers. The MCA uses tSNE projection, which prioritizes finding local communities, while in this paper UMAP was used, which focuses preserving the global structure of the data. A side effect of the mathematical differences between these methods is that intercluster distance does not have much meaning in tSNE space, but reflects variation in a UMAP projection. Consequently, separation by organ in a UMAP projection as seen here does indeed support the claim of the manuscript. However, the strong separation of samples derived from the same organ (e.g. the two kidney samples) undermines this reasoning, as it implies the presence of a batch effect aiding in the separation. This is not sufficiently addressed by the authors.

We have confirmed the separation between organ fibroblasts observed in our single-cell data by cross-validating with the Mouse Cell Atlas data. Three independent clustering methods were employed (Author response image 5), including UMAP (Panel A), t-SNE (Panel B) and PHATE (PMID:31796933, Panel C), all showing a clear separation between organ fibroblasts. The separation between two kidney subpopulations were also confirmed, which is consistent with renal fibroblasts literature (https://journals.biologists.com/dev/article/147/15/dev190108/143923/Identification-and-characterization-of-cellular; https://www.nature.com/articles/s41467-022-28226-7).

Author response image 5
Visualisation of single-cell organ fibroblasts data from the Mouse Cell Atlas (PMID:29474909), using different clustering methods: (A) UMAP, (B) t-SNE, and (C) PHATE.
https://doi.org/10.7554/eLife.71008.sa2

Article and author information

Author details

  1. Elvira Forte

    The Jackson Laboratory, Bar Harbor, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Visualization, Writing – original draft, Writing – review and editing
    Contributed equally with
    Mirana Ramialison and Hieu T Nim
    For correspondence
    elviraforte83@gmail.com
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5555-9122
  2. Mirana Ramialison

    1. Australian Regenerative Medicine Institute, Monash University, Clayton, Australia
    2. Systems Biology Institute Australia, Clayton, Australia
    3. Murdoch Children’s Research Institute, The Royal Children’s Hospital, Parkville, Australia
    Contribution
    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Visualization, Supervision, Visualization, Writing – original draft, Writing – review and editing
    Contributed equally with
    Elvira Forte and Hieu T Nim
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6315-4777
  3. Hieu T Nim

    1. Australian Regenerative Medicine Institute, Monash University, Clayton, Australia
    2. Systems Biology Institute Australia, Clayton, Australia
    3. Murdoch Children’s Research Institute, The Royal Children’s Hospital, Parkville, Australia
    Contribution
    Data curation, Formal analysis, Investigation, Methodology, Visualization, Validation, Visualization, Writing – original draft, Writing – review and editing
    Contributed equally with
    Elvira Forte and Mirana Ramialison
    Competing interests
    No competing interests declared
  4. Madison Mara

    The Jackson Laboratory, Bar Harbor, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  5. Jacky Y Li

    Murdoch Children’s Research Institute, The Royal Children’s Hospital, Parkville, Australia
    Contribution
    Data curation, Formal analysis
    Competing interests
    No competing interests declared
  6. Rachel Cohn

    The Jackson Laboratory, Farmington, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  7. Sandra L Daigle

    The Jackson Laboratory, Bar Harbor, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  8. Sarah Boyd

    Centre for Inflammatory Diseases, School of Clinical Sciences at Monash Health, Monash University, Clayton, Australia
    Contribution
    Formal analysis, Supervision
    Competing interests
    No competing interests declared
  9. Edouard G Stanley

    1. Murdoch Children’s Research Institute, The Royal Children’s Hospital, Parkville, Australia
    2. Department of Paediatrics, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Australia
    3. Department of Anatomy and Developmental Biology, Monash University, Clayton, Australia
    Contribution
    Formal analysis, Supervision
    Competing interests
    No competing interests declared
  10. Andrew G Elefanty

    Murdoch Children’s Research Institute, The Royal Children’s Hospital, Parkville, Australia
    Contribution
    Resources, Supervision
    Competing interests
    No competing interests declared
  11. John Travis Hinson

    1. The Jackson Laboratory, Farmington, United States
    2. Cardiology Center, UConn Health, Farmington, United States
    Contribution
    Formal analysis, Supervision
    Competing interests
    No competing interests declared
  12. Mauro W Costa

    1. The Jackson Laboratory, Bar Harbor, United States
    2. Australian Regenerative Medicine Institute, Monash University, Clayton, Australia
    Contribution
    Data curation, Formal analysis, Funding acquisition, Investigation, Supervision, Validation, Visualization
    Competing interests
    No competing interests declared
  13. Nadia A Rosenthal

    1. The Jackson Laboratory, Bar Harbor, United States
    2. Australian Regenerative Medicine Institute, Monash University, Clayton, Australia
    3. National Heart and Lung Institute, Imperial College London, London, United Kingdom
    Contribution
    Funding acquisition, Supervision, Writing – original draft, Writing – review and editing
    Contributed equally with
    Milena B Furtado
    Competing interests
    No competing interests declared
  14. Milena B Furtado

    1. The Jackson Laboratory, Bar Harbor, United States
    2. Australian Regenerative Medicine Institute, Monash University, Clayton, Australia
    Present address
    Merck Research Laboratories, South San Francisco, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review and editing
    Contributed equally with
    Nadia A Rosenthal
    For correspondence
    milenabfurtado@gmail.com
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1387-325X

Funding

Australian Research Council

  • Nadia A Rosenthal

National Health and Medical Research Council

  • Mirana Ramialison
  • Mauro W Costa
  • Nadia A Rosenthal

Heart Foundation

  • Mirana Ramialison

Jackson Laboratory

  • Nadia A Rosenthal

National Institutes of Health

  • Nadia A Rosenthal

Leducq Foundation for Cardiovascular Research

  • Nadia A Rosenthal

Australian Government

  • Nadia A Rosenthal

State Government of Victoria

  • Nadia A Rosenthal

Innovation Fund (P20 GM104318)

  • Nadia A Rosenthal

NIH/NIA (U01 AG022308-17)

  • Nadia A Rosenthal

NIH/NCI (P30 CA034196)

  • Nadia A Rosenthal

The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.

Acknowledgements

We gratefully acknowledge Dr. Guoji Guo’s lab for providing the scRNAseq stromal cell dataset, and the contribution of Luis E Lima, Heidi Munger, Philipp Heinrich, and the Genome Technology, Single Cell, Light Microscopy, Flow cytometry, and Computational Sciences Services at The Jackson Laboratory for service provided. This work is supported by grants from the Australian Research Council (ARC) and the National Health and Medical Research Council (NHMRC) to NAR, MWC, MR, and NHMRC/Heart Foundation Fellowship to MR; and by the JAX Director’s Innovation Fund, and the NIH/NIGMS (2 P20 GM104318), the NIH/NIA (5 U01 AG022308-17), and grant from the Leducq Foundation for Cardiovascular Research to NR. The Australian Regenerative Medicine Institute is supported by grants from the State Government of Victoria and the Australian Government. The Jackson Laboratory scientific services are supported by the NIH/NCI (5 P30 CA034196).

Ethics

Human subjects: Human samples were obtained through the Sydney Heart Bank (SHB) in Australia. Investigators have not collected patient samples or been privy to patient records.

This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved Institutional Animal Care and Use Committee (IACUC) protocol (#16010) of the Jackson Laboratory. All surgery was performed under tribromoethanol anesthesia, and every effort was made to minimize pain and suffering.

Senior and Reviewing Editor

  1. Paul W Noble, Cedars-Sinai Medical Center, United States

Publication history

  1. Preprint posted: June 4, 2021 (view preprint)
  2. Received: June 4, 2021
  3. Accepted: March 15, 2022
  4. Accepted Manuscript published: March 16, 2022 (version 1)
  5. Version of Record published: March 28, 2022 (version 2)

Copyright

© 2022, Forte et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Elvira Forte
  2. Mirana Ramialison
  3. Hieu T Nim
  4. Madison Mara
  5. Jacky Y Li
  6. Rachel Cohn
  7. Sandra L Daigle
  8. Sarah Boyd
  9. Edouard G Stanley
  10. Andrew G Elefanty
  11. John Travis Hinson
  12. Mauro W Costa
  13. Nadia A Rosenthal
  14. Milena B Furtado
(2022)
Adult mouse fibroblasts retain organ-specific transcriptomic identity
eLife 11:e71008.
https://doi.org/10.7554/eLife.71008
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