Difference between revisions of "Single cell Omics"
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(Created page with "* [https://satijalab.org/seurat/ Seurat] - R toolkit for single cell genomics * [https://www.10xgenomics.com/resources/datasets/ 10x genomics datasets] * [https://www.youtube....") |
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| + | ==Introduction== | ||
| + | * Part of the standard repertoire of biological research techniques | ||
| + | * Look at heterogeneity | ||
| + | * avoids caveat of bulk averaging | ||
| + | * heterogenous tissue | ||
| + | * cell number | ||
| + | * developmental stages | ||
| + | * allows inference of dynamic processes | ||
| + | * multiple asynchronous states of a cell states | ||
| + | * transcriptional profile that happens across those time stages | ||
| + | * interrogate potential mechanisms at cellular resolution | ||
| + | |||
| + | ==Sample Prep== | ||
| + | # Solid tissue | ||
| + | # dissociation | ||
| + | # single cell isolation | ||
| + | # generate cdna library | ||
| + | # amplify cdna productcuts ususlally PCR | ||
| + | * droplet-based gel cell barcode | ||
| + | * plate-based sc-Seq using flow cytometry - allows gating to target cells of interest | ||
| + | ** full length transcript allows get isoform analysis | ||
| + | * 10xgenomics prep guidelines | ||
| + | ** single cell atac-seq important to. start with their protocols and buffers | ||
| + | * Try for 90% viability threshold | ||
| + | * minimize dead cells nucleic acids inhibitors of reverse transcription | ||
| + | * Miltenyi dead cell removal | ||
| + | * single NUCLEI sequencing - option for difficult to dissociate tissue, strongly-adherent, fragile cells like neurons, or solid frozen samples | ||
| + | ** More agnostic to variability of selection bias | ||
| + | ** But lower RNA content | ||
| + | ** higher introns retained | ||
| + | |||
| + | |||
| + | ===Add-on Modalities=== | ||
| + | |||
| + | * VDJ sequencing solution from 10x genomics | ||
| + | ** TCR-VCR sequencing | ||
| + | ** TCR clonotypes grouped by t-SNE | ||
| + | * Antibody feature barcoding | ||
| + | ** cell surface protein coding | ||
| + | ** similar to FACS | ||
| + | ** limited to surface proteins, can't get at transcription factors with this technique | ||
| + | * Sample multiplexing | ||
| + | ** same as tagging sample subtypes, but have different barcoding | ||
| + | ** can associate which original sample they came from | ||
| + | ** run all the samples together in the sequencing run, reduces technical variation for biological replicates, more statistical power | ||
| + | |||
| + | ==Analysis Toolkits== | ||
| + | * Loupe Cell Browser (free from 10x genomics) | ||
| + | * Partek flow (14-day free trial) | ||
| + | * FloJo SeqGeq | ||
* [https://satijalab.org/seurat/ Seurat] - R toolkit for single cell genomics | * [https://satijalab.org/seurat/ Seurat] - R toolkit for single cell genomics | ||
| − | |||
* [https://www.youtube.com/watch?v=NEaUSP4YerM t-SNE presentation on statquest] | * [https://www.youtube.com/watch?v=NEaUSP4YerM t-SNE presentation on statquest] | ||
| + | * [https://nih-irp-singlecell.github.io NIH Single Cell Community] | ||
| + | |||
| + | ==Public datasets==* [https://www.10xgenomics.com/resources/datasets/ 10x genomics datasets] | ||
Revision as of 13:37, 23 May 2019
Introduction
- Part of the standard repertoire of biological research techniques
- Look at heterogeneity
- avoids caveat of bulk averaging
- heterogenous tissue
- cell number
- developmental stages
- allows inference of dynamic processes
- multiple asynchronous states of a cell states
- transcriptional profile that happens across those time stages
- interrogate potential mechanisms at cellular resolution
Sample Prep
- Solid tissue
- dissociation
- single cell isolation
- generate cdna library
- amplify cdna productcuts ususlally PCR
- droplet-based gel cell barcode
- plate-based sc-Seq using flow cytometry - allows gating to target cells of interest
- full length transcript allows get isoform analysis
- 10xgenomics prep guidelines
- single cell atac-seq important to. start with their protocols and buffers
- Try for 90% viability threshold
- minimize dead cells nucleic acids inhibitors of reverse transcription
- Miltenyi dead cell removal
- single NUCLEI sequencing - option for difficult to dissociate tissue, strongly-adherent, fragile cells like neurons, or solid frozen samples
- More agnostic to variability of selection bias
- But lower RNA content
- higher introns retained
Add-on Modalities
- VDJ sequencing solution from 10x genomics
- TCR-VCR sequencing
- TCR clonotypes grouped by t-SNE
- Antibody feature barcoding
- cell surface protein coding
- similar to FACS
- limited to surface proteins, can't get at transcription factors with this technique
- Sample multiplexing
- same as tagging sample subtypes, but have different barcoding
- can associate which original sample they came from
- run all the samples together in the sequencing run, reduces technical variation for biological replicates, more statistical power
Analysis Toolkits
- Loupe Cell Browser (free from 10x genomics)
- Partek flow (14-day free trial)
- FloJo SeqGeq
- Seurat - R toolkit for single cell genomics
- t-SNE presentation on statquest
- NIH Single Cell Community
==Public datasets==* 10x genomics datasets
