A transcriptome atlas of leg muscles from healthy human volunteers reveals molecular and cellular signatures associated with muscle location
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Evaluation Summary:
This extensive study dissects the different gene expression patterns in a large set of different human lower limb muscles. It is an extensive transcriptome study. Its potential importance is that it points out insights into their differing changes in particular muscle diseases associated with specific gene defects.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)
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Abstract
Skeletal muscles support the stability and mobility of the skeleton but differ in biomechanical properties and physiological functions. The intrinsic factors that regulate muscle-specific characteristics are poorly understood. To study these, we constructed a large atlas of RNA-seq profiles from six leg muscles and two locations from one muscle, using biopsies from 20 healthy young males. We identified differential expression patterns and cellular composition across the seven tissues using three bioinformatics approaches confirmed by large-scale newly developed quantitative immune-histology procedures. With all three procedures, the muscle samples clustered into three groups congruent with their anatomical location. Concomitant with genes marking oxidative metabolism, genes marking fast- or slow-twitch myofibers differed between the three groups. The groups of muscles with higher expression of slow-twitch genes were enriched in endothelial cells and showed higher capillary content. In addition, expression profiles of Homeobox ( HOX ) transcription factors differed between the three groups and were confirmed by spatial RNA hybridization. We created an open-source graphical interface to explore and visualize the leg muscle atlas ( https://tabbassidaloii.shinyapps.io/muscleAtlasShinyApp/ ). Our study reveals the molecular specialization of human leg muscles, and provides a novel resource to study muscle-specific molecular features, which could be linked with (patho)physiological processes.
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Evaluation Summary:
This extensive study dissects the different gene expression patterns in a large set of different human lower limb muscles. It is an extensive transcriptome study. Its potential importance is that it points out insights into their differing changes in particular muscle diseases associated with specific gene defects.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)
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Reviewer #1 (Public Review):
The authors report on their quite extensive study to dissect the differences in gene expression in different human lower limb muscles to explain individual differences between different muscles in the human body, not the least in order to explain the specific and often selective differences in how muscles react on gene defects in different myopathies. The intentions are well taken and the setup is ambitious with the collection of six different muscles from each of the 20 individuals.
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Reviewer #2 (Public Review):
Abbassi-Daloii et al. investigated the molecular and cellular signatures associated with heterogeneity among human leg muscles using RNA-sequencing and immune-histology phenotypic characterization. They analysed 128 biopsies sampled from 6 leg muscles of 20 young healthy male individuals: two muscles from the hamstrings (semitendinosus (ST) and gracilis (GR)), three from the quadriceps (rectus femoris (RF), vastus lateralis (VL) and vastus medialis (VM)) and one lower leg muscle (gastrocnemius lateralis (GL)). They also analysed the middle and distal parts of the semitendinosus (STM and STD). Using the expression of known markers of specific cell types, they show that muscles cluster into three main groups based on cell type composition: group 1 (G1: GR, STM, and STD), group 2 (G2: RF, VL, and VM) and group …
Reviewer #2 (Public Review):
Abbassi-Daloii et al. investigated the molecular and cellular signatures associated with heterogeneity among human leg muscles using RNA-sequencing and immune-histology phenotypic characterization. They analysed 128 biopsies sampled from 6 leg muscles of 20 young healthy male individuals: two muscles from the hamstrings (semitendinosus (ST) and gracilis (GR)), three from the quadriceps (rectus femoris (RF), vastus lateralis (VL) and vastus medialis (VM)) and one lower leg muscle (gastrocnemius lateralis (GL)). They also analysed the middle and distal parts of the semitendinosus (STM and STD). Using the expression of known markers of specific cell types, they show that muscles cluster into three main groups based on cell type composition: group 1 (G1: GR, STM, and STD), group 2 (G2: RF, VL, and VM) and group 3 (G3: GL). Interestingly, these groups correspond to their anatomical location of origin. Group 1 was enriched in markers of fast-twitch muscle fibres, while Groups 2 and 3 were enriched in markers of endothelial cells and slow-twitch muscle fibres. Muscles clustered similarly after excluding genes related to cell type composition, indicating that a difference in cell-type composition between muscles is not the only factor accounting for their different molecular signatures. They further show that genes related to oxidative phosphorylation and mitochondria-associated metabolic processes are enriched in groups 2 and 3, consistent with their higher expression of markers of slow-twitch fibres and associated oxidative metabolism.
In addition, the authors developed a high-throughput quantitative immunohistochemistry procedure to measure the relative expression of specific myosin heavy chain isoforms on muscle cryosections from the same biopsies. They suggest that muscle fibres can be separated into three main clusters according to the expression of MyHC1 (slow-twitch fibres), MyHC2A (fast-twitch fibres type 2A) and MyHC2X (fast-twitch fibres type 2X). They show that myofibres from groups 2 and 3 are enriched in slow-twitch fibres compared to group 1, in agreement with the gene expression data, and that muscles from groups 2 and 3 can be discriminated according to their relative composition of type 2A fibres.
Gene expression and immunohistochemistry data showed that the GL muscle has a higher blood vessel density compared to other muscles and that HOX genes have a differential expression pattern among the analysed muscles.
These data provide an impressive dataset of human muscle gene expression from young healthy individuals and will serve as a reference for future investigation of muscle pathology or ageing. The conclusions of this paper are mostly well supported by the data, but some aspects of the analyses by immunohistochemistry need to be clarified.
- The method to cluster fibres according to their relative expression of myosin isoforms needs to be further explained (Figure 3). Fibres were clustered into three main categories according to the fluorescence intensity of MyHC1, MyHC2A and MyHC2X after immunohistochemistry. Figure 3B shows a continuum of the three intensities rather than clearly separated clusters and the authors should explain how fluorescence intensity thresholds were used to define such clusters. In addition, the differences in average Mean Fluorescence Intensity (MFI) values between clusters appear quite low: Max(MyHC1)/Min(MyHC1)=0.725/0.653=1.11;
Max(MyH2A)/Min(MyH2A)=0.806/0.535=1.50;
Max(MyH2X)/Min(MyH2X)=0.718/0.651=1.10.
These differences are low, notably compared to the gene expression data where these myosin isoforms have expression levels spread over 2 logs (Figures 3D-F). The authors should show specific examples of quantification, show specific quantification of fluorescence over MFI of background, and add statistics of expression differences based on MFI for each MyHC between clusters.
Figures 3D-F show correlations between the % of fibres in a given fluorescence cluster as a function of the expression by RNAseq of the corresponding MyHC defining this cluster. To strengthen these correlations, the authors should show the % of fibres in a given fluorescence cluster as a function of the expression of all three MyHC isoforms (e.g. % of fibres in cluster 1 = f(expression MYH7) or f(expression MYH1), same for clusters 2 and 3).
The authors assess blood vessel density in GL by immunohistochemistry (Figure 4). Figure 4A does not mention the muscle corresponding to the cryosection presented, and presenting STM and GL sections side-by-side would help to understand the conclusions of this figure. Displaying the green and red arrows as well on the CD31 and ENG panels and showing higher magnifications would help to understand which regions were defined or not as capillaries.
The authors validate HOX expression patterns by RNA-scope (Figure 8). Figure 8A does not mention the muscle corresponding to the cryosection presented. Maybe it would help show STM and GL sections stained for HOXA10 and HOXC10 side-by-side. Also, although the authors mention that signal specificity of RNA-scope probes was verified using negative controls, it would be helpful to show these controls and validate these probes on muscle sections known not to express HOX genes (head-derived?).
Finally, gene expression datasets from human muscle samples have already been generated. As discussed by the authors, these studies were limited in terms of the number of samples, large variation of donor ages, sample conservation before processing, etc. Nevertheless, it would be helpful to put the main findings of this paper (cell type composition, blood vessel density, fibre types ratios, HOX genes expression, mitochondrial processes, etc) into context and assess, if possible and if the data is available, whether similar findings can be concluded from previous datasets.
- The method to cluster fibres according to their relative expression of myosin isoforms needs to be further explained (Figure 3). Fibres were clustered into three main categories according to the fluorescence intensity of MyHC1, MyHC2A and MyHC2X after immunohistochemistry. Figure 3B shows a continuum of the three intensities rather than clearly separated clusters and the authors should explain how fluorescence intensity thresholds were used to define such clusters. In addition, the differences in average Mean Fluorescence Intensity (MFI) values between clusters appear quite low: Max(MyHC1)/Min(MyHC1)=0.725/0.653=1.11;
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