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.

Spatial Proteomic Approach to Characterize Skeletal Muscle Myofibers.

Skeletal muscle myofibers have differential protein expression resulting in functionally distinct slow- and fast-twitch types. While certain protein classes are well-characterized, the depth of all proteins involved in this process is unknown. We utilized the Human Protein Atlas (HPA) and the HPASubC tool to classify mosaic expression patterns of staining across 49,600 unique tissue microarray (TMA) images using a visual proteomic approach. We identified 2164 proteins with potential mosaic expression, of which 1605 were categorized as "likely" or "real." This list included both well-known fiber-type-specific and novel proteins. A comparison of the 1605 mosaic proteins with a mass spectrometry (MS)-derived proteomic dataset of single human muscle fibers led to the assignment of 111 proteins to fiber types. We additionally used a multiplexed immunohistochemistry approach, a multiplexed RNA-ISH approach, and STRING v11 to further assign or suggest fiber types of newly characterized mosaic proteins. This visual proteomic analysis of mature skeletal muscle myofibers greatly expands the known repertoire of twitch-type-specific proteins.

A liposome-encapsulated spin trap for the detection of nitric oxide.

Electron paramagnetic resonance (EPR) is one of the few methods that allows for the unambiguous detection of nitric oxide (NO). However, the dithiocarbamate-iron spin traps employed with this method inhibit the activity of nitric oxide synthase and catalyze NO production from nitrite. These disadvantages limit EPR's application to biological NO detection. We present a liposome-encapsulated spin-trap (LEST) method for the capture and in situ detection of NO by EPR. The method shows a linear response for [NO]≥4µM and can detect [NO]≥40nM in a 500µL sample (≥20 pmol). The kinetics of NO production can be followed in real time over minutes to hours. LEST does not inhibit the activity of inducible nitric oxide synthase or nitrate reductase and shows minimal abiotic NO production in the presence of nitrite and NADH. Nitrate reductase-like activity is detected in cell lysates of the coccolithophore Emiliania huxleyi and is elevated in virus-infected culture. This method shows particular promise for NO detection in cell lysates and crude preparations of NO-producing tissues.