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Background
Intramuscular fat (IMF), the white adipose tissue deposited between skeletal muscle fibers, is a key determinant of beef quality due to its contribution to meat flavor, juiciness, and tenderness. However, IMF develops later and grows more slowly, compared to other fat depots such as subcutaneous fat (SF) in cattle. The cellular and molecular mechanisms underlying the delayed development and slow growth of IMF remain poorly understood. We hypothesized that later development and slower growth of IMF compared to SF may, in part, arise from the differences in their progenitor cells.
Results
We performed single-cell RNA sequencing (scRNA-seq) on the stromal vascular fractions (SVFs) from IMF and SF of adult Angus crossbred steers as well as the mononuclear cell fractions (MCFs) from skeletal muscles of newborn Angus crossbred bull calves, with each tissue type collected from two animals. A total of 14,802 cells from 6 animals were sequenced. Clustering analysis revealed that these cells comprised ten cell types, including adipose progenitor cells (APCs), muscle satellite cells (MuSCs), myoblasts, smooth muscle cells, and various immune cell populations. The SF-derived SVF from adult cattle harbored a significantly higher proportion of APCs than the IMF-derived SVF. The MCFs from newborn calves did not contain detectable APCs. Subclustering analysis revealed that the APCs comprised six subpopulations (C0–C5), among which C3 and C5 were absent in the IMF-derived SVF while C1 was markedly less abundant in the IMF-derived SVF than in the SF-derived SVF. Gene set variation analysis and pseudotime trajectory analysis showed that C1 and C3 represented more differentiated APCs, with higher expression of genes involved in adipogenesis, such as PPARG, ADAM12, and PPARGC1A, whereas subclusters C0 and C4 represented undifferentiated, uncommitted APCs, with higher expression of genes involved in DNA replication and cell adhesion, compared to the other subclusters.
Conclusions
Overall, this single-cell transcriptomics study suggests two potential differences in APCs between IMF and SF in adult cattle: (1) IMF contains fewer APCs than SF; (2) APCs in IMF are adipogenically less committed and less differentiated compared to APCs in SF. These differences may partially explain why IMF develops later and grows more slowly than SF in cattle. This study also suggests that, in cattle, intramuscular fat begins to develop postnatally, challenging the widely held belief that it forms during late gestation.
Background
Intramuscular fat (IMF), also known as marbling, refers to the white adipose tissue deposited between muscle fibers within skeletal muscle and is a key determinant of beef quality, directly influencing its flavor, juiciness, tenderness, and visual appeal [1, 2]. In contrast, subcutaneous fat (SF) is considered less valuable than IMF because it does not directly contribute to meat quality and because consumers in general avoid eating too much fat [3]. Increasing the deposition of IMF without simultaneously raising undesirable fat depots such as SF in cattle remains a major challenge in beef cattle production, highlighting the need to better understand the depot-specific mechanisms governing adipose tissue development and growth.
Although both IMF and SF originate from mesenchymal stromal cells (MSCs), increasing evidence suggests that they have different developmental trajectories [4, 5]. Specifically, IMF formation is much later compared to SF and other fat depots [6, 7, 8, 9–10]. In cattle, IMF adipocytes appear later than those in visceral, subcutaneous, and intermuscular depots during embryogenesis, and their preadipocytes continue to proliferate for an extended period after birth [11, 12–13]. In addition to its delayed formation, IMF also exhibits a slower growth rate than SF during the postnatal life, as reflected by its much lower allometric growth coefficient (~ 0.80) compared to SF in cattle (~ 1.20) [14, 15–16]. Compared to SF, IMF adipocytes exhibit lower lipogenic activity and reduced metabolic efficiency during hypertrophy [9, 17, 18].
What causes the delayed formation and slower growth of IMF compared to SF remains incompletely understood. Several studies, including our own recent work, have shown that preadipocytes from IMF exhibit lower differentiative capacities than those from SF [7, 19, 20, 21, 22–23], suggesting that the delayed development and the slower growth of IMF compared to SF may be due to the reduced adipogenic differentiation potential of preadipocytes from the former. We hypothesized that the delayed development and the slower growth of IMF may also result from a delayed commitment of MSCs to adipogenic lineage and/or a delayed differentiation of adipose progenitors into preadipocytes within IMF. To test this hypothesis, we employed single-cell RNA sequencing (scRNA-seq) to profile the adipose progenitor cells (APCs) in IMF and SF of adult cattle as well as mononuclear cell populations in skeletal muscle of neonatal calves, aiming to investigate depot-specific differences in adipogenic commitment and differentiation.
Materials and methods
Animals and collection of tissue samples
The experimental use of animals in this project received approval from the Institutional Animal Care and Use Committee at Virginia Tech (IACUC #20–169). Longissimus dorsi muscle (LDM) samples (~ 500 g each) were collected from two 1-day-old bull calves at euthanasia. LDM (~ 500 g for IMF isolation) and SF (~ 10 g) samples were collected at slaughter from four grain-finished steers (approximately 18 months of age), with IMF and SF each obtained from two different animals. The IMF and SF samples for scRNA-seq were collected from different animals rather than from the same individuals, for practical reasons. The isolation of sufficient and high-quality SVF cells from adipose tissue for scRNA-seq was a time-consuming and labor-intensive procedure (see details below). Due to the duration and complexity of this procedure, it was not feasible to complete the same procedures for both IMF and SF samples from a single animal on the same day. All cattle, including two newborn calves and four adult steers, were produced from Angus dams and Simmental sires and raised at the Virginia Tech Beef Center, and euthanized by captive-bolt stunning followed by exsanguination. Immediately after collection, tissue samples were placed in pre-chilled phosphate-buffered saline (PBS) and transported to the laboratory on ice. In the lab, IMF was carefully isolated from the LDM with scissors and forceps. Muscle tissue visible to the naked eye was carefully removed from IMF.
Isolation of stromal vascular fraction from adipose tissue
Stromal vascular fractions (SVFs) from steer IMF and SF were isolated using a previously described protocol [24], with minor modifications. Both SF and IMF tissues were minced into ~ 1 mm³ fragments in pre-chilled PBS and then digested with 2 mg/mL collagenase type I (Thermo Fisher Scientific, Waltham, MA, USA; Cat# 17100017) for 1 h at 37 °C in a shaking incubator. The cell suspension was filtered through a 100 μm strainer and centrifuged at 500 g for 15 min at 4 °C. The cell pellet was resuspended in 1× red blood cell lysis buffer (Thermo Fisher Scientific; Cat# 00–4333−57) at room temperature for 5 min to lyse any remaining erythrocytes, followed by a second centrifugation. The pellet from this final centrifugation was resuspended in Dulbecco’s Modified Eagle Medium (DMEM, Thermo Fisher Scientific; Cat# 11995065) containing 10% fetal bovine serum (FBS, Atlanta Biologicals, Lawrenceville, GA, USA; Cat# PS-100).
Isolation of mononuclear cell fractions from muscle tissue
Mononuclear cell fractions (MCFs) from newborn calf LDM samples were isolated using previously published methods [25, 26]. Muscle samples were washed thoroughly with PBS to remove blood and debris. The cleaned muscle was ground using a sterile metal meat grinder. The ground muscle was digested with 1 mg/mL pronase (Thermo Fisher Scientific; Cat# 882703) and 1 mg/mL collagenase type I for 1 h at 37 °C in a shaking incubator. The digestion mixture was centrifuged at 1500 g for 10 min at 4 °C. The supernatant was collected and passed through a 100 μm strainer to eliminate undigested tissue fragments. The filtrate was then centrifuged at 500 g for 15 min at 4 °C to pellet the cells. The cell pellet was subsequently treated with 1× red blood cell lysis buffer (Thermo Fisher Scientific; Cat# 00–4333−57) at room temperature for 5 min to lyse any remaining erythrocytes, followed by a second centrifugation to obtain a purified MCF population. The pellet from this centrifugation was resuspended in DMEM containing 10% FBS.
Cell debris and dead cell removal
Freshly isolated SVF and MCF cells were subjected to debris and dead cell removal procedures to improve sample purity and viability for downstream applications. First, tissue debris was removed using the Debris Removal Solution (Miltenyi Biotec, Auburn, CA, USA; Cat# 130-109−398), following the manufacturer’s protocol. Briefly, cell suspensions were centrifuged at 300 g for 10 min at 4℃ and resuspended in cold PBS. Debris removal solution was added and mixed by pipetting slowly up and down. The suspension was carefully overlaid with cold PBS to avoid disturbing phase separation and then centrifuged at 3,000 g for 10 min at 4 °C. The top two layers were aspirated and discarded. The cell pellet was washed with cold PBS, centrifuged again at 1,000 g for 10 min, and resuspended in PBS.
Dead cells were removed using the Dead Cell Removal Kit (Miltenyi Biotec, Cat# 130-090−101), following the manufacturer’s instructions. In brief, cell suspension was centrifuged at 300 g for 10 min at 4℃ and supernatant was aspirate completely. Cells were resuspended in 100 µL of dead cell removal microbeads per 10⁷ total cells and incubated for 15 min at room temperature. After labeling, cells were washed with 1× binding buffer and passed through an MS Column (Miltenyi Biotec, Cat# 130-042−201) placed in a MACS Separator (Miltenyi Biotec). Live cells were collected in the flow-through, while dead cells retained in the column were discarded. The purified viable cell fraction was subsequently centrifuged and resuspended in PBS containing 0.04% bovine serum albumin (BSA, Thermo Fisher Scientific; Cat# 15260037) for viability assessment and scRNA-seq.
ScRNA-seq library preparation and sequencing
Cells resuspended in PBS containing 0.04% BSA were passed through a 40 μm cell strainer to eliminate cell clumps. Cell concentration and viability were assessed by trypan blue (Thermo Fisher Scientific, Cat# 15250061) staining and Countess II Automated Cell Counter (Thermo Fisher Scientific). All samples showed viability > 85%. ScRNA-seq libraries were prepared using the Chromium Single Cell 3′ Reagent Kits v3 (10x Genomics, Pleasanton, CA, USA; Cat# 1000075) according to the manufacturer’s protocol. Briefly, cell suspensions were adjusted to a concentration of 1,000 cells/µL using PBS containing 0.04% BSA. For each library, 12.8 µL of cell suspension was mixed with a reverse transcription (RT) master mix and loaded onto a Chromium Chip B (10x Genomics; Cat# 1000074). Single cell Gel Bead-in-Emulsion (GEM) generation, barcoding, and reverse transcription were performed on the Chromium Controller (10x Genomics). Within each GEM, a single cell was lysed, and barcoded oligonucleotides released from the gel bead were used to label the reverse-transcribed cDNA from that cell. Barcoded cDNAs were amplified by PCR for 11 cycles. Library size distribution and fragment quality were assessed using an Agilent High Sensitivity TapeStation (Agilent Technologies, Santa Clara, CA, USA). scRNA-seq libraries were sequenced on an Illumina NovaSeq 6000 at the Novogene-UC Davis Sequencing Center (Novogene, Sacramento, CA, United States) to generate paired-end reads of 150 bp, according to the 10x Genomics protocol for Single Cell 3′ v3 libraries.
Preprocessing, alignment, and quality control of scRNA-seq sequencing data
Sequencing data were first aligned to the bovine reference genome (Bos_taurus.ARS-UCD1.2) using the Cell Ranger (v3.0.2) (10x Genomics) count process with default parameters [27]. The noise brought by the ambient RNA contamination was removed using CellBender (v0.2.0) [28]. The output h5 files from CellBender were uploaded to Seurat (5.2.0) for downstream analyses [29]. In Seurat, the h5 files were first filtered using the nFeatures (300 to 3000 genes per cell) and nCounts (< 10,000 UMI per cell) programs. After counts normalization, high variable feature identification, data scaling and PCA analysis, data from 6 scRNA-seq libraries were integrated using the Harmony algorithm to enable comparative analysis between sources of cells [30]. To further eliminate low-quality cells, the contamination score was estimated for each scRNA-seq library using decontX (1.4.0) [31]. The cells with contamination scores >0.2 were removed from the dataset, and the remaining cells were clustered using the RunUMAP function in Seurat with the parameters dims = 1:30 and seed.use = 45.
Cell type annotation
Cell type was annotated based on canonical marker gene expression. The cluster-specific marker genes were identified using the FindAllMarkers function in Seurat. Genes with > 25% expression rate within cluster, adjusted p-value < 0.05 and log2 fold change > 1 were used as the marker genes representing the given cluster. Cluster identity was manually assigned by comparing the identified cluster-specific marker genes to known cell type–specific markers reported in the literature.
To ensure robustness and reduce subjectivity in cell type classification, we also annotated cell types using ScType, an automated cell type annotation tool [32]. ScType was implemented using default settings with custom-defined marker gene sets for relevant bovine cell types. Each cluster was annotated with the cell type with the highest ScType score.
Visualization of cluster-specific marker genes and functional enrichment analysis
To exhibit the top 50 marker genes in each cluster, 20 cells were randomly selected with seed.use = 45 from each library. After scaling the expression level range from − 1 to + 1, the expression levels of these genes were shown in a heatmap. Two functional enrichment analyses, over regulation analysis (ORA) and Gene Set Variation Analysis (GSVA), were implemented via the clusterProfiler (4.10.1) [33] and GSVA (1.50.5) R package [34], respectively. In ORA both the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases were used, while in GSVA only the GO database was used.
Developmental trajectory and differentiation potential analyses
Developmental trajectory analysis was performed using the Monocle3 (1.3.7) program, essentially following its official instruction [35]. Briefly, the data for APC subclusters in IMF and SF were extracted from the combined dataset, and developmental trajectory graphs were constructed using the parameters minimal_branch_len = 10 and 15; ncenter = 300 and 150, respectively. Developmental potential was evaluated using CytoTRACE (0.3.3) with default parameters [36].
Results
Integrated scRNA-seq analysis identifies diverse cell types in the stromal vascular fraction of intramuscular fat and subcutaneous fat and mononuclear cell fraction of muscle
A total of six scRNA-seq libraries were prepared for SVF of intramuscular fat (M1 and M2 from two adult steers) and subcutaneous fat (S1 and S2 from another two different adult steers), as well as MCFs of skeletal muscle from two neonatal calves (C5 and C6). Sequencing these libraries generated a total of 1.04 billion PE150 reads. Based on the Cell Ranger analysis, a total of 32,240 cells were sequenced from these libraries. After quality control filtering and removal of ambient RNA contamination, 14,802 high-quality cells were retained for downstream analysis. Table 1 summarizes the key sequencing and mapping metrics for each scRNA-seq library.
Table 1. Summary of sequencing and mapping metrics for scRNA-seq libraries
Sample ID | Total Raw Reads (in million) | Estimated Cell Number | Mean Counts Per Cell | Mean Genes Per Cell | Total Genes Detected | Median UMI Counts Per Cell | Sequencing Saturation (%) |
|---|---|---|---|---|---|---|---|
M1 | 206.25 | 1049 | 196,619 | 1376 | 16,297 | 2812 | 89.2 |
M2 | 77.45 | 621 | 124,724 | 508 | 13,649 | 739 | 91 |
S1 | 315.33 | 6697 | 47,085 | 1507 | 18,134 | 3361 | 78.1 |
S2 | 92.67 | 10,597 | 8745 | 91 | 14,630 | 106 | 94.1 |
C5 | 89.92 | 9700 | 9205 | 658 | 16,387 | 926 | 53 |
C6 | 259.25 | 3576 | 72,499 | 1501 | 16,826 | 2702 | 87.6 |
*C calf muscle MCF, M adult IMF SVF, S adult SF SVF
Analyzing the sequencing data from the 14,802 cells in Seurat [29] revealed that they comprised ten major cell types (Fig. 1). Based on marker genes identified using the FindAllMarkers function in Seurat, these cell types were manually annotated as muscle satellite cell or MuSC (with a representative marker gene PAX7), myoblast (MYOG), adipose progenitor cell or APC (PDGFRA), blood vessel endothelial cell or BEC (FLT1), smooth muscle cell or SMC (ACTA2), T cell (PTPRC), macrophage (F13A1), mast cell (FCER1G), lymphatic endothelial cell or LEC (ETS1), and Schwann cell (MPZ) (Fig. 2). These cell types were consistent with those annotated using ScType (data not shown), an automated cell annotation tool [32].
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Fig. 1
Experimental workflow and UMAP visualization of cell types. A Experimental workflow for single-cell RNA sequencing: samples were collected from intramuscular fat, subcutaneous fat of 18-month-old steers (n = 2 per group), and longissimus dorsi muscle of 1-day-old calves (n = 2); then stromal vascular fraction (SVF) and mononuclear cell fractions (MCF) were isolated, followed by library generation and sequencing with the 10X platform. B UMAP visualization of cell types in stromal vascular fractions from subcutaneous fat and intramuscular fat of adult cattle and mononuclear cell fractions from skeletal muscle of newborn calves. Ten distinct clusters of cells were identified and annotated based on canonical marker genes. BEC: blood vascular endothelial cell; LEC: lymphatic endothelial cell; APC: adipose progenitor cell; MuSC: muscle satellite cell; SMC: smooth muscle cell
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Fig. 2
Expression of representative marker genes in different cell clusters. A Feature plots showing the spatial expression of representative marker genes across all identified clusters in the UMAP space. Representative marker genes are shown under cell types. B Violin plots showing the expression levels of representative marker genes across cell clusters. BEC: blood vascular endothelial cell; LEC: lymphatic endothelial cell; APC: adipose progenitor cell; MuSC: muscle satellite cell; SMC: smooth muscle cell
Functional enrichment analyses confirm cell identity
To validate the cell type annotated above, we extracted the top 50 marker genes from each cluster—selected based on higher log2 fold change and expression percentage within each cluster—and visualized their expression patterns in a heatmap (Fig. 3). In this heatmap, the x-axis displays distinct cell types, while the y-axis lists marker genes, each represented by a colored tile whose color matches that of the corresponding cell type on the x-axis. The cluster-specific marker genes were relatively restricted within the corresponding cell type, indicating the top cluster-specific marker genes exhibited the characteristics of a given cell type. To further validate the characteristics of each cluster, functional enrichment analysis was performed on the top 50 marker genes from each cluster using the GO or KEGG pathway database. These analyses revealed distinct and biologically relevant pathway enrichments across clusters/cell types. The clusters of MuSCs and myoblasts showed enriched expression of genes functioning in pathways related to musculoskeletal movement and cytoskeleton in muscle cells. The cluster of APCs had enriched expression of genes involved in extracellular matrix organization. The cluster of BECs had enriched expression of genes involved in cell adhesion and focal adhesion. The cluster of SMCs showed enriched expression of genes involved in vascular smooth muscle contraction, while the clusters of T cells and macrophages had enriched expression of genes participating in the immune response-related pathways including T cell receptor signaling, JAK-STAT signaling, phagosome formation, and chemokine signaling. The cluster of mast cells had enriched expression of genes involved in pathways associated with mast cell activation and cellular oxidant detoxification. The cluster of LECs showed enriched expression of genes involved in tight junction and Rap1 signaling pathways. The cluster of Schwann cells had enriched expression of genes involved in myelination. These enrichment profiles confirm the biological relevance of the clustering and the selection of marker genes for each cluster (Fig. 3).
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Fig. 3
Functional enrichment in each cell cluster/type. GO and KEGG pathway enrichment analysis was performed using the top 50 marker genes for each cell cluster/type. BEC: blood vascular endothelial cell; LEC: lymphatic endothelial cell; APC: adipose progenitor cells; MuSC: muscle satellite cell; SMC: smooth muscle cell
Subcutaneous fat stromal vascular fraction contains more adipose progenitors than intramuscular fat stromal vascular fraction
We compared cell type distribution between the intramuscular fat SVF (IMF-SVF) and the subcutaneous fat SVF (SF-SVF). A UMAP projection revealed both common and depot-specific cell populations in the two fat depots (Fig. 4A). Both depots contained a population of APCs, but the SF-SVF had markedly more APCs than the IMF-SVF. The percentage of mast cells was also significantly enriched in the SF-SVF compared to the IMF-SVF. In contrast, blood vessel endothelial cells were more abundant in the IMF-SVF. The detailed proportions of each cell type in the SF-SVF and IMF-SVF are shown in Fig. 4B.
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Fig. 4
Cell composition differs between intramuscular fat (IMF) and subcutaneous fat (SF) stromal vascular fraction (SVF) from adult cattle. A UMAP plots showing the distribution of cell populations in SVF of IMF and SF. B Stacked bar plot showing the relative proportions of different cell types in SVF of IMF and SF. BEC: blood vascular endothelial cell; LEC: lymphatic endothelial cell; APC: adipose progenitor cell; MuSC: muscle satellite cell; SMC: smooth muscle cell
Adipose progenitor cells exhibit distinct subcluster identities between subcutaneous and intramuscular fat
To compare the APCs between IMF and SF, we extracted their transcriptomic data from the entire scRNA-seq dataset and performed reclustering based on their transcriptomic profiles. This analysis identified six APC subclusters, designated C0 to C5 (Fig. 5A). Transcriptomic comparison revealed that subclusters C3 and C5 were absent while C1 was present at a markedly lower proportion in the IMF-derived APCs (5%) compared to the SF-derived APCs (30%) (Fig. 5B).
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Fig. 5
Subclustering reveals distinct subpopulations of adipose progenitor cells in stromal vascular fraction (SVF) of adult cattle intramuscular fat (IMF) and subcutaneous fat (SF). A UMAP plot showing six transcriptionally distinct subclusters (C0–C5) of APCs. Dashed circles indicate adipogenically more inclined subclusters (C1, C3, and C5), which are enriched in SF-SVF but largely absent or reduced in IMF-SVF. B Stacked bar plot displaying the percentages of each APC subcluster in IMF and SF SVF
To further investigate the functional characteristics of each APC subcluster, we performed a Gene Set Variation Analysis (GSVA) [34]. This analysis revealed distinct pathway activity scores (i.e., GSVA scores) for the six clusters (Fig. 6), reflecting their transcriptional heterogeneity and different lineage states. Subclusters C1 and C3 had enriched expression of genes involved in pathways related to white fat cell differentiation, positive regulation of adipose tissue development, lipid phosphorylation, and fatty acid beta-oxidation, indicating that these two APC subpopulations are transcriptionally more committed toward adipogenic differentiation than the remaining subclusters. In contrast, subclusters C0 and C4 showed signatures of early progenitor-like or proliferative states, such as enriched expression of genes involved in DNA replication, cell cycle initiation, and endodermal cell fate specification, as well as Notch signaling and cell adhesion regulation, suggesting that they represent less differentiated and possibly more plastic subpopulations of APCs. Subcluster C2 was characterized by enriched expression of genes involved in extracellular matrix remodeling and cell–cell junction pathways, including gap junction-mediated intercellular transport and integrin-mediated adhesion, suggesting that cells in this subcluster play a more important role in organizing the adipose tissue. Meanwhile, subcluster C5 displayed enriched expression of genes involved in vascular remodeling, smooth muscle-associated genes, and fatty acid transport across membranes, pointing to a perivascular cell-like identity for this subpopulation of APCs.
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Fig. 6
Functional heterogeneity among subclusters of adipose progenitor cells. Shown in heatmap are enrichment scores of functional terms for each APC subcluster (C0–C5). These scores were determined by a Gene Set Variation Analysis (see text for details)
Consistent with the GSVA results, marker gene analysis revealed distinct molecular features for each APC subcluster (Figs. 7 and 8). As visualized in the annotated marker gene distribution plots (Fig. 7), subcluster C1 showed high expression of PPARG, ADAM12, AK5, and C7, which are associated with adipogenic commitment, supporting its classification as a preadipocyte-like population. Subcluster C3 showed elevated expression of PPARGC1A, PLD1, PFKP, and ACOX3, genes involved in mitochondrial function and lipid metabolism, suggesting a metabolically more active state. Subclusters C0 and C4, marked by enriched expression of INTS6 and NCALD, respectively, and as indicated in Fig. 7, contained fewer uniquely expressed genes (13 and 20 marker genes, respectively) compared to other subclusters, and lacked expression of genes involved in lineage-specific pathways, indicating a less differentiated or earlier progenitor status. Subcluster C2 had enriched expression of fibroblast-associated genes, including ELN, BGN, MFAP4, and FBLN1, suggesting a fibroblast-like phenotype in this APC subpopulation. Subcluster C5 uniquely expressed APOE and vascular smooth muscle-related genes such as ACTA2, TAGLN, and MYH11, supporting its classification as a subpopulation of perivascular-like APCs.
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Fig. 7
Distribution of marker genes across adipose progenitor cell subclusters. Each panel represents one APC subcluster (C0–C5), with dots indicating individual marker genes. The x-axis shows the percentage of cells within the subcluster expressing the gene, and the y-axis indicates the log2 fold change of expression in this subcluster compared to other subclusters. The number under each subcluster denotes the total number of marker genes identified for this subcluster
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Fig. 8
Dot plot showing the expression patterns of representative marker genes (shown in parentheses) across six subclusters (C1 to C6) of adipose progenitors
Differentiation potential and pseudotime analyses reveal that adipose progenitor cells from intramuscular fat are less differentiated than those from subcutaneous fat
To investigate the developmental dynamics and lineage progression of APCs in IMF and SF, we conducted both CytoTRACE [36] and Monocle3 [35] analyses. CytoTRACE was first applied to infer differentiation potential based on transcriptional diversity, where lower CytoTRACE scores indicate undifferentiated, progenitor-like states and higher scores correspond to more differentiated cell identities. As shown in Fig. 9A, subclusters C0 and C4 exhibited lower CytoTRACE scores (blue to green), consistent with a less differentiated, stem-like status of these cells compared to the other subclusters. In contrast, subclusters C1, C2, and C3 displayed higher CytoTRACE scores (yellow to red), reflecting their more differentiated states.
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Fig. 9
Differentiation states of cells in the subclusters of adipose progenitor cells (APCs). A CytoTRACE analysis showing predicted differentiation potential. Lower CytoTRACE scores (blue-green) indicate more primitive, stem-like states, while higher scores (red-yellow) indicate more differentiated states. B Monocle3 trajectory plot illustrating pseudotime progression of cells in the subclusters of APCs. The black line represents the inferred developmental path of cells, starting from the root and branching toward different endpoints
Next, we employed Monocle3 to construct a pseudotime trajectory among the 6 subclusters of APCs. As shown in Fig. 9B, based on this trajectory, which was a branched structure, subclusters C0 and C4 were located near the root of the trajectory, indicating their early progenitor status. Subclusters C1 and C3 were positioned at the termini of two branches, consistent with their more terminally differentiated and metabolically more active states, respectively. Subcluster C2 occupied an intermediate position, possibly representing their fibroblast-like transitional states. Notably, most of the IMF-derived APCs were located at earlier pseudotime stages, whereas the SF-derived APCs spanned a broader range of pseudotime stages, including more differentiated endpoints (Fig. 9B). This pseudotime trajectory further supports the notion that intramuscular adipogenic progenitors are adipogenically less differentiated and less mature compared to their subcutaneous counterparts.
Adipose progenitor cells are absent in the skeletal muscle of newborn calves
Whereas newborn calves contained subcutaneous fat, they did not have any visible intramuscular fat (data not shown). To determine whether newborn calves have intramuscular adipose progenitors, we included MCFs derived from the longissimus dorsi muscle of 1-day-old calves in this scRNA-seq study. As expected, muscle MCFs from newborn calves contained abundant MuSCs, myoblasts and Schwann cells (Fig. 10), reflecting the skeletal muscle origin of these cells. However, they did not contain a population of APCs (Fig. 10), indicating that APCs are either absent or rare in the skeletal muscle of newborn calves.
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Fig. 10
Absence of adipose progenitor cells in mononuclear cell fractions (MCFs) from newborn calf skeletal muscle compared to abundant presence of these cells in stromal vascular fraction (SVF) from adult cattle intramuscular fat (IMF). A UMAP plots showing the distribution of cell types in adult IMF SVF and newborn calf skeletal muscle MCF. B Stacked bar plot showing the relative proportions of different cell types in the two tissues. MuSC: muscle satellite cell; APC: adipose progenitor cell; BEC: blood vascular endothelial cell; LEC: lymphatic endothelial cell; SMC: smooth muscle cell
Discussion
Single-cell RNA sequencing is a recently invented technique powerful for identification of rare cell types, dynamic cell states, and cell-cell interactions within complex tissues [37]. In this study, we employed scRNA-seq to profile gene expression in IMF and SF tissues of adult beef cattle as well as skeletal muscle tissue of newborn calves. Our analysis revealed that the two adipose tissues mainly contain APCs forming mature adipocytes, smooth muscle cells and endothelial cells forming blood vessels, and various immune cell types, and that the skeletal muscle tissue contains mainly satellite cells and myoblasts forming muscle fibers. These identified cell populations are largely consistent with those reported in previous scRNA-seq studies of bovine adipose and skeletal muscle tissues [38, 39, 40, 41–42]. In addition, we identified a small population of Schwann cells in the skeletal muscle tissue. Schwann cells are glial cells responsible for forming myelin sheaths around peripheral axons [43]. Their detection supports the sensitivity and accuracy of our scRNA-seq approach, as it confirms the presence of expected, yet relatively rare, cell types within the skeletal muscle tissue.
Based on the proportions of APCs in the SVFs of IMF and SF, IMF appears to contain fewer APCs than SF in adult cattle. This difference in APC abundance seems to contradict the greater abundance of preadipocytes in IMF versus SF found in a previous scRNA-seq study [38]. The reason for this discrepancy remains unclear. However, fewer APCs in IMF versus SF can partially explain why IMF develops later and grows more slowly compared to SF in cattle [6, 7, 8, 9, 10, 11, 12–13, 17, 18, 19, 20, 21, 22–23]. The presence of fewer APCs in IMF may result from fewer MSCs committed to APCs in IMF than in SF or slower proliferation of APCs in IMF than in SF. Either possibility may reflect fundamental differences in the tissue microenvironment and physiological roles of IMF and SF. SF is anatomically located beneath the skin and primarily functions as an energy reservoir, supporting continuous adipocyte turnover and expansion [44, 45]. In contrast, IMF is embedded within skeletal muscle and is directly influenced by factors from the surrounding myofibers. Skeletal myofibers secrete many signaling proteins called myokines, several of which possess anti-adipogenic properties, such as myostatin [46, 47–48], interleukin-6 [49, 50–51], irisin [52], oxytocin [53, 54], and fibroblast growth factor 21 [55]. It is possible that one or more of these myokines constrain MSCs from committing to APCs, i.e., the adipogenic lineage.
In addition to the reduced abundance of APCs in IMF, our analysis revealed that APCs in IMF are transcriptionally less differentiated compared to those in SF. By performing subclustering of the entire APC population, we identified six transcriptionally distinct subpopulations (C0–C5), reflecting a spectrum of adipogenic differentiation states. Among these, subclusters C1 and C3 exhibited features of adipogenically committed progenitors. Subcluster C1 had a high expression of classical adipogenic-related genes such as PPARG [56, 57], ADAM12 [58] AK5 [59] and C7 [60], indicating active adipogenic differentiation. Subcluster C3 was enriched for lipid metabolic and mitochondrial genes including PPARGC1A [61, 62–63], PLD1 [64, 65], PFKP [66], ACOX3 [67], and GAPDH [68], consistent with a metabolically active preadipocyte-like identity. In contrast, subclusters C0 and C4 lacked expression of definitive lineage markers but had enriched expression of genes involved in pathways such as DNA replication and cell adhesion, suggesting an undifferentiated, uncommitted state. Weinreb et al. pointed out that fate bias in multipotent progenitors (MPPs) occurs earlier than the detectable expression of lineage marker genes [69]. Subcluster C2 was enriched for fibroblast-associated features and expressed multiple extracellular matrix (ECM)–related genes, including ELN [70], BGN [71], MFAP4 [72], COL8A1 [73]. Subcluster C5 was defined as perivascular-related adipogenic progenitors, due to the enrichment of pathways associated with vascular development and the expression of smooth muscle–related genes such as ACTA2 [74], TAGLN [75], and MYH11 [76]. It also expressed lipid metabolism–related genes including APOE [77] and IGFBP7 [78], indicating a dual role in vascular structure and adipogenesis.
Notably, IMF-derived APCs had much fewer cells belonging to the subclusters C1 and C3, adipogenically more inclined subpopulations, compared to SF-derived APCs. In contrast, most IMF APCs were enriched at earlier developmental stages and exhibited higher differentiation potential scores, consistent with a less differentiated, progenitor-like state. These findings suggest that adipose progenitor cells in IMF are developmentally less mature and less committed toward the adipogenic lineage compared to those in SF. This may be another reason why IMF develops later and grows more slowly compared to SF in cattle [7, 21, 23]. These depot-specific differences are likely driven by distinct microenvironmental cues, such as myokine signaling [79, 80], extracellular matrix composition [81, 82], and metabolic activity [83, 84].
Our results also indicate that MCFs from skeletal muscle of neonatal calves lack APCs. This result suggests that most if not all APCs for IMF are generated postnatally. This notion appears to contradict the commonly held view that most APCs for IMF and other fat depots in cattle are determined before birth [11, 12–13, 22, 85]. However, almost all of these review articles describe developmental timelines of fat depots in cattle based on general assumptions or indirect evidence [22, 85, 86, 87–88]. To our knowledge, only one research article provided immunofluorescent evidence of mature adipocytes in fetal skeletal muscle after 180 days of gestation [89]. Although this study detected lipid-filled adipocytes in fetal skeletal muscle by immunofluorescence, it did not provide evidence ruling out the possibility that the detected adipocytes were anatomically intermuscular. In that study [89], the authors interpreted the upregulation of SCD, PPARG, and CEBPB in skeletal muscle as further evidence of IMF adipocyte differentiation. However, this interpretation is limited by the use of bulk tissue RNA, which cannot rule out the possibility that these gene expression changes originate from cells other than IMF adipocytes. There is a possibility that intramuscular APCs exist at extremely low abundance at birth, falling below the detection threshold of scRNA-seq or being excluded during quality control filtering steps in scRNA-seq data analysis.
Our proposition that bovine intramuscular APCs emerge postnatally rather than prenatally is supported by a scRNA-seq study of human skeletal muscle across developmental stages, which found that APCs are absent during embryonic and fetal phases but become detectable and increase in abundance after birth [90]. We speculate that the postnatal appearance of intramuscular APCs may result from the postnatal changes in the skeletal muscle secretome. Skeletal muscle is a prolific secretory organ, releasing a diverse array of myokines, which can influence the fate of neighboring stromal cells [91]. The composition of this secretome is not static; it is shaped by muscle activity, metabolic state, and inflammation, which all change with birth [92]. It is therefore reasonable to hypothesize that the transition from the fetal to postnatal stage induces a fundamental shift in muscle myokine secretion, establishing a permissive niche for the emergence and expansion of intramuscular APCs.
Beyond adipogenic progenitors, we also observed notable differences in immune and endothelial cell composition between IMF and SF, which may influence depot-specific adipogenic capacity. IMF-SVF exhibited higher proportions of T cells, macrophages, and BECs, indicating a more immunologically active microenvironment and potentially greater vascularization in IMF compared to SF. Previous studies have shown that T cell and macrophage infiltration in perimuscular fat is associated with impaired adipose expansion and metabolic dysfunction [93]. On the other hand, the enrichment of BECs in IMF underscores the essential role of vascularization in intramuscular fat development and growth [94, 95–96]. Intramuscular adipocytes reside in close proximity to capillaries [97], suggesting that vascular structures provide essential support for their development through nutrient delivery and angiocrine signaling [98, 99]. In beef cattle, increased vascularization is positively associated with marbling scores, and highly marbled breeds such as Wagyu exhibit greater capillary density compared to leaner breeds [41]. In contrast, SF exhibited higher levels of mast cells, which have been implicated in adipose tissue remodeling, angiogenesis, and metabolic regulation [100, 101–102] and may help maintain adipogenesis in that fat depot. Together, these observations underscore the importance of depot-specific immune and vascular features landscapes may contribute to the distinct adipogenic capacities of IMF and SF.
The findings of this work have practical implications for the beef industry. For example, the molecular signatures of APC subpopulations we uncovered offer novel targets for genetic selection and nutritional strategies to predictably and efficiently enhance marbling. Furthermore, our finding that the differentiation of intramuscular APCs may be constrained by muscle-secreted factors suggests that strategies targeting these factors could be effective in enhancing marbling deposition without causing excess adipose accumulation in other locations.
Limitations of the study
This study has several limitations. First, the number of animals used was too small for statistical analysis. The observed cell type distributions and transcriptional patterns may not fully capture the population-level variability among cattle. Future studies using larger cohorts and additional time points will be necessary to validate and generalize these findings. Second, the conclusions regarding progenitor identity and differentiation potential are based primarily on transcriptomic signatures. Functional validations, such as lineage tracing or in vitro differentiation assays, will be required to confirm these inferences.
Conclusion
This scRNA-seq study suggests two potential differences between IMF and SF in adult cattle: IMF harbors fewer APCs than SF, and APCs in IMF are adipogenically less committed and differentiated than those in SF. These differences may in part contribute to the delayed development and slower growth of IMF compared to SF in cattle. In addition, this study suggests that IMF forms postnatally, not prenatally, as widely believed.
Acknowledgements
Not applicable.
Authors’ contributions
Z.T. designed and conducted the experiments, analyzed the data, and wrote the manuscript. P.L. analyzed the data and revised the manuscript. H.J. conceived the study, designed the experiments, and revised the manuscript. All authors read and approved the final manuscript.
Funding
This study was supported by the Agriculture and Food Research Initiative competitive grant 2021-67015‐33398 from the USDA National Institute of Food and Agriculture (to H.J.).
Data availability
All data generated or analyzed during this study are included in this published article. The sequencing data from this study has been deposited in the NCBI GEO database (https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE305686.
Declarations
Ethics approval and consent to participate
The experimental use of animals in this project received approval from the Institutional Animal Care and Use Committee at Virginia Tech (IACUC #20–169).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Abbreviations
Intramuscular fat
Subcutaneous fat
Stromal vascular fraction
Mononuclear cell fractions
Muscle satellite cell
Peroxisome proliferator-activated receptor gamma
CCAAT/enhancer binding protein alpha
Fatty acid binding protein 4
Fibro/adipogenic progenitor
Adipose progenitor cell
Single-cell RNA sequencing
Unique molecular identifier
Quality control
Principal component analysis
Uniform manifold approximation and projection
Mesenchymal stromal cell
Endothelial cell
Smooth muscle cell
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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