Single cell RNA-seq analysis of the flexor digitorum brevis mouse myofibers
BACKGROUND: Skeletal muscle myofibers can be separated into functionally distinct cell types that differ in gene and protein expression. Current single cell expression data is generally based upon single nucleus RNA, rather than whole myofiber material. We examined if a whole-cell flow sorting approach could be applied to perform single cell RNA-seq (scRNA-seq) in a single muscle type.
METHODS: We performed deep, whole cell, scRNA-seq on intact and fragmented skeletal myofibers from the mouse fast-twitch flexor digitorum brevis muscle utilizing a flow-gated method of large cell isolation. We performed deep sequencing of 763 intact and fragmented myofibers.
RESULTS: Quality control metrics across the different gates indicated only 171 of these cells were optimal, with a median read count of 239,252 and an average of 12,098 transcripts per cell. scRNA-seq identified three clusters of myofibers (a slow/fast 2A cluster and two fast 2X clusters). Comparison to a public skeletal nuclear RNA-seq dataset demonstrated a diversity in transcript abundance by method. RISH validated multiple genes across fast and slow twitch skeletal muscle types.
CONCLUSION: This study introduces and validates a method to isolate intact skeletal muscle myofibers to generate deep expression patterns and expands the known repertoire of fiber-type-specific genes.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:11 |
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| Enthalten in: |
Skeletal muscle - 11(2021), 1 vom: 17. Mai, Seite 13 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Verma, Rohan X [VerfasserIn] |
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| Links: |
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| Themen: |
Fiber |
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| Anmerkungen: |
Date Completed 19.01.2022 Date Revised 19.01.2022 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1186/s13395-021-00269-2 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM325524734 |
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| 100 | 1 | |a Verma, Rohan X |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Single cell RNA-seq analysis of the flexor digitorum brevis mouse myofibers |
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| 500 | |a Date Revised 19.01.2022 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a BACKGROUND: Skeletal muscle myofibers can be separated into functionally distinct cell types that differ in gene and protein expression. Current single cell expression data is generally based upon single nucleus RNA, rather than whole myofiber material. We examined if a whole-cell flow sorting approach could be applied to perform single cell RNA-seq (scRNA-seq) in a single muscle type | ||
| 520 | |a METHODS: We performed deep, whole cell, scRNA-seq on intact and fragmented skeletal myofibers from the mouse fast-twitch flexor digitorum brevis muscle utilizing a flow-gated method of large cell isolation. We performed deep sequencing of 763 intact and fragmented myofibers | ||
| 520 | |a RESULTS: Quality control metrics across the different gates indicated only 171 of these cells were optimal, with a median read count of 239,252 and an average of 12,098 transcripts per cell. scRNA-seq identified three clusters of myofibers (a slow/fast 2A cluster and two fast 2X clusters). Comparison to a public skeletal nuclear RNA-seq dataset demonstrated a diversity in transcript abundance by method. RISH validated multiple genes across fast and slow twitch skeletal muscle types | ||
| 520 | |a CONCLUSION: This study introduces and validates a method to isolate intact skeletal muscle myofibers to generate deep expression patterns and expands the known repertoire of fiber-type-specific genes | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Fiber | |
| 650 | 4 | |a Single cell RNA-sequencing | |
| 650 | 4 | |a Skeletal muscle | |
| 650 | 4 | |a Twitch | |
| 700 | 1 | |a Kannan, Suraj |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Brian L |e verfasserin |4 aut | |
| 700 | 1 | |a Fomchenko, Katherine M |e verfasserin |4 aut | |
| 700 | 1 | |a Nieuwenhuis, Tim O |e verfasserin |4 aut | |
| 700 | 1 | |a Patil, Arun H |e verfasserin |4 aut | |
| 700 | 1 | |a Lukban, Clarisse |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Xiaoping |e verfasserin |4 aut | |
| 700 | 1 | |a Fox-Talbot, Karen |e verfasserin |4 aut | |
| 700 | 1 | |a McCall, Matthew N |e verfasserin |4 aut | |
| 700 | 1 | |a Kwon, Chulan |e verfasserin |4 aut | |
| 700 | 1 | |a Kass, David A |e verfasserin |4 aut | |
| 700 | 1 | |a Rosenberg, Avi Z |e verfasserin |4 aut | |
| 700 | 1 | |a Halushka, Marc K |e verfasserin |4 aut | |
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