Why does viability assessment matter for single-cell transcriptomics?

Viability assessment using dyes helps optimize dissociation conditions to preserve RNA quality and minimize cell loss, ensuring accurate representation of tissue composition. Maintaining at least 80 percent viable cells supports reliable downstream sequencing and reduces technical noise in gene expression data. This step is critical for generating high-quality cDNA libraries from primary tissues.

How does tissue dissociation impact single-cell RNA sequencing outcomes?

General dissociation is essential for achieving high cell yields and an accurate representation of tissue composition, while excessive mechanical or enzymatic stress can bias cellular populations. The protocol balances dissociation efficiency with preservation of RNA quality by optimizing incubation times and trituration steps. This approach minimizes artifacts that could confound interpretation of gene expression changes in disease or injury models.

What role does filtration play in preparing single-cell suspensions?

Filtering tissue samples through a 40 micrometer filter removes clumps and debris, ensuring a true single-cell suspension for accurate sequencing analysis. Rinsing the filter with BSA/HBSS helps recover cells and reduce loss during processing. This step is vital for preventing clogging in microfluidic systems and improving data quality in droplet-based platforms.

Why is FACS sorting used after dissociation in this workflow?

FACS isolation of viable and healthy cells using narrow-bottom tubes and BSA/HBSS washes helps remove debris and enrich for intact cells prior to sequencing. The method includes steps to prevent static and surface tension by inverting collection tubes and washing sorted cells to improve recovery. This enrichment step enhances the proportion of high-quality cells entering the single-cell controller, improving library complexity.

How does bioinformatic analysis support biological interpretation in this protocol?

Post-sequencing analysis using Seurat enables quality control through visualization of gene counts, UMIs, and mitochondrial gene percentages to identify doublets and outliers. Clustering resolution is adjusted to distinguish major cell types from transitional states, with low resolution used for heat maps and high resolution for sub-type detection. Tools like Monocle further support trajectory inference and validation of mRNA distribution norms.

드롭릿 바코드 기반 단일 셀 Transcriptomics 성인 포유류 직물의

19.7K 조회수

인용 6회

10:12 분

2019년 1월 10일

10.3791/58709-v

2019년 1월 10일

19.7K 조회수

Jo Anne Stratton^*¹^,²^,³ , Sarthak Sinha^*² , Wisoo Shin² , Elodie Labit² , Tak-Ho Chu¹^,⁴ , Prajay T. Shah² , Rajiv Midha¹^,⁴ , Jeff Biernaskie¹^,²^,³

¹Hotchkiss Brain Institute, University of Calgary, ²Department of Comparative Biology and Experimental Medicine, Faculty of Veterinary Medicine, University of Calgary, ³Alberta Children's Hospital Research Institute, University of Calgary, ⁴Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary

^* These authors contributed equally

이 프로토콜에서는 일반적인 프로세스를 설명 합니다 및 품질 관리 검사 드롭릿 기반, 높은 처리량 단일 셀 RNA-Seq 준비에 대 한 건강 한 성인 포유류 단일 세포를 준비에 필요한. 시퀀싱 매개 변수, 읽기 정렬 및 다운스트림 단일 셀 bioinformatic 분석 또한 제공 됩니다.