Journal: Nature methods

Article Title: Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing

doi: 10.1038/s41592-021-01153-z

Figure Lengend Snippet: a) Standard droplet-based scRNA-seq, where cells are loaded at a low concentration (limiting dilution) to avoid cell doublets, and most droplets do not receive a cell. b) scifi-RNA-seq, which uses preindexing and droplet overloading to boost the throughput of droplet-based scRNA-seq. c) Detailed method design of scifi-RNA-seq. d) Representative images of droplets containing between one and ten nuclei, showing the overloading of a standard microfluidic droplet generator (10x Genomics Chromium). e) Droplet overloading boosts the percentage of droplets filled with nuclei from 16.4% (obtained for the maximum loading concentration of the standard Chromium protocol) to 95.5% (obtained for 100-fold overloading using 1.53 million nuclei per channel). f) Droplet overloading causes the average number of nuclei per droplet to increase in a controlled fashion while maintaining the desired Poisson-like loading distribution. g) Expected collision rate as a function of the cell/nuclei loading concentration per channel for standard droplet-based scRNA-seq and for scifi-RNA-seq with different numbers of round1 barcodes. h) Due to the high number of microfluidic (round2) barcodes, scifi-RNA-seq exceeds the barcoding capability of three-round combinatorial indexing protocols.

Article Snippet: Pre-indexed cells or nuclei are pooled and encapsulated into emulsion droplets using a standard microfluidic droplet generator (10x Genomics Chromium).

Techniques: Concentration Assay, RNA Sequencing

Journal: Nature methods

Article Title: Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing

doi: 10.1038/s41592-021-01153-z

Figure Lengend Snippet: a-b) Representative microscopy images of droplets (top rows) and histograms showing the number of nuclei per droplet (bottom rows) at different loading concentrations (15,300, 191,000, 383,000, 765,000, and 1,530,000 nuclei per channel) for the Chromium scATAC v.1.0 chip (panel a) and for the scATAC v.1.1 Next GEM chip (panel b). To obtain these images, lysis reagents were omitted from the cell loading experiment, and a total of 3,265 (scATAC v.1.0) or 4,509 (scATAC v.1.1 Next GEM) droplets were manually counted. Moreover, the number of beads per droplet (rightmost image and diagram) was visualized and counted based on a loading experiment in which the nuclei suspension was substituted by 1x Nuclei Buffer, while Reducing Agent B was omitted. c) Despite substantial droplet overloading, stable droplet emulsions were obtained for all tested conditions. d) Box plots showing the droplet diameters for the Chromium scATAC v.1.0 and scATAC v.1.1 Next GEM microfluidic chips at different loading concentrations. For each setup, 100 droplets were evaluated. Box plots depict the interquartile range with marked median and whiskers extending to 1.5 times the interquartile range. e) Histogram showing droplet diameters (as in panel d) pooled across different loading concentrations (500 droplets per platform).

Article Snippet: Pre-indexed cells or nuclei are pooled and encapsulated into emulsion droplets using a standard microfluidic droplet generator (10x Genomics Chromium).

Techniques: Microscopy, Lysis, Suspension

Journal: Nature methods

Article Title: Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing

doi: 10.1038/s41592-021-01153-z

Figure Lengend Snippet: a) Droplet overloading boosts the percentage of droplets filled with nuclei for the scATAC v.1.1 Next GEM microfluidic chip. b) Droplet overloading on the scATAC v.1.1 Next GEM chip increases the average number of nuclei per droplet in a controlled fashion, while maintaining the desired Poisson-like loading distribution. c) Expected collision rates on the Next GEM chip as a function of the loaded number of cells or nuclei per channel for standard droplet-based scRNA-seq and for scifi-RNA-seq with different numbers of round1 barcodes. The cell/nuclei fill rate was modeled as a zero-inflated Poisson distribution. d-f) Modeling of the microfluidic device loading using alternative distributions (Negative Binomial, Poisson, Zero Inflated Negative Binomial, Zero Inflated Poisson). The number of loaded nuclei is plotted against the number of nuclei per droplet on a linear scale (panel d), logarithmic scale (panel e), and as point estimates (panel f). g) Statistical properties of the distribution of nuclei per droplet across experiments. The relationship between mean and variance that is expected for a Poisson distribution is indicated by gray lines. h) Computational modeling of droplet loading as a zero-inflated Poisson function. i) Posterior probability distributions of lambda and psi sampled using a Markov Chain Monte Carlo (MCMC) analysis. j) Independent estimation of the cell doublet rates using Monte Carlo simulations. Error bars in panels d, e, h, and j indicate three standard deviations around the mean.

Article Snippet: Pre-indexed cells or nuclei are pooled and encapsulated into emulsion droplets using a standard microfluidic droplet generator (10x Genomics Chromium).

Techniques: RNA Sequencing

Journal: Nature methods

Article Title: Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing

doi: 10.1038/s41592-021-01153-z

Figure Lengend Snippet: a) Schematic outline of scifi-RNA-seq including detailed oligonucleotide sequences. The reverse transcription is performed inside permeabilized cells or nuclei on a 96-well or 384-well plate, introducing well-specific round1 barcodes into the whole transcriptome. Pre-indexed cells or nuclei are pooled and encapsulated into emulsion droplets using a standard microfluidic droplet generator (10x Genomics Chromium). The round2 barcodes are introduced by thermocycling ligation with a complementary bridge oligo and thermostable ligase. The droplet emulsion is then broken, and a second defined end is introduced into the library via template switching. cDNA is enriched and tagmented with a custom i7-only transposome. Finally, the library is PCR-enriched, with the option to introduce an additional sample index. The read structure for next-generation sequencing on the Illumina No-vaSeq 6000 and NextSeq 500 platforms is shown. b) Nuclei recovery after pre-indexing of the whole transcriptome by reverse transcription. scifi-RNA-seq achieves high recovery rates for both cell lines and primary material. c) Nuclei with pre-indexed transcriptome, prior to microfluidic device loading, visualized under a microscope in a counting chamber. The selected image (representative of two replicate samples) shows nuclei derived from human primary T cells. d) Typical size distribution of enriched cDNA obtained with scifi-RNA-seq. e) Typical size distribution of final scifi-RNA-seq libraries ready for next-generation sequencing. f) Distribution of DNA bases along scifi-RNA-seq sequencing reads, showing the characteristic sequence patterns of the UMI, round1 barcode, sample barcode, round2 barcode, and transcript. g) Heatmap showing sequencing quality (Qscore) for each sequencing cycle.

Article Snippet: Pre-indexed cells or nuclei are pooled and encapsulated into emulsion droplets using a standard microfluidic droplet generator (10x Genomics Chromium).

Techniques: RNA Sequencing, Reverse Transcription, Emulsion, Ligation, Introduce, Next-Generation Sequencing, Microscopy, Derivative Assay, Sequencing

Journal: Nature methods

Article Title: Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing

doi: 10.1038/s41592-021-01153-z

Figure Lengend Snippet: a) Performance metrics for scifi-RNA-seq experiments using a mixture of human Jurkat cells and mouse 3T3 cells, starting from whole cells permeabilized by methanol, freshly isolated nuclei, and nuclei fixed with 1% or 4% formaldehyde (cryopreserved, re-hydrated, and permeabilized). The following plots are shown: (1) ranked barcodes plotted against reads, unique molecular identifiers (UMIs), and detected genes, distinguishing singlecell transcriptomes from background noise; (2) reads plotted against UMIs; (3) reads plotted against the number of detected genes; (4) reads plotted against the fraction of unique reads; (5) species mixing plot showing the number of UMIs per cell aligning to the mouse genome (x-axis) versus the human genome (y-axis). To facilitate comparisons between the different types of input material, the axes of the performance plots use the same scale across conditions. b) In a species mixing experiment with pre-indexed nuclei from human (Jurkat) and mouse (3T3) cells run at the maximum loading concentration of the standard Chromium protocol (15,300 nuclei per channel), the microfluidic round2 barcode (left plot) is sufficient to resolve single cells. Nevertheless, the combination of round1 and round2 barcodes still improves the separation (right plot). c) Coverage along human and mouse transcripts from 200 bp upstream of the transcription start site (TSS) to 200 bp downstream of the transcription end site (TES), shown for whole cells permeabilized by methanol, freshly isolated nuclei, and nuclei fixed with 1% or 4% formaldehyde (cryopreserved, re-hydrated, and permeabilized). Freshly isolated nuclei show the strongest 3’ enrichment. d) Box plots summarizing sequence alignment metrics across the different types of input material: Total reads sequenced, percent uniquely mapped reads, percent multi-mappers, percent alignments to exons plus introns, percent alignments to exons, and percent spliced reads. Freshly isolated nuclei showed the best performance for these alignment metrics. The box plots summarize a total of 2,299 whole cells; 2,000 fresh nuclei; 2,051 nuclei fixed with 1% formaldehyde and 1,896 nuclei fixed with 4% formaldehyde. Box plots depict the interquartile range with marked median and whiskers extending to 1.5 times the interquartile range.

Article Snippet: Pre-indexed cells or nuclei are pooled and encapsulated into emulsion droplets using a standard microfluidic droplet generator (10x Genomics Chromium).

Techniques: RNA Sequencing, Isolation, Concentration Assay, Sequencing

Journal: Nature methods

Article Title: Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing

doi: 10.1038/s41592-021-01153-z

Figure Lengend Snippet: a) ’Knee plot’ showing the number of UMIs (y-axis) per barcode ranked by frequency (x-axis) for scifi-RNA-seq on the Chromium scATAC v.1.0 chip versus the scATAC v.1.1 Next GEM chip. The characteristic inflection points are indicated, which separate cells/nuclei (left, colored lines) from background noise (right, grey lines). b) Reads per cell plotted against UMIs per cell to assess the level of sequencing saturation for the two microfluidic chips. c) Reads per cell plotted against the unique read fraction per cell to assess PCR duplication and library complexity for the two microfluidic chips. d) Alignments to the human genome versus alignments to the mouse genome in the species mixing experiment to assess the frequency of cell doublets for the two microfluidic chips. e) Alignment metrics for the two microfluidic chips. f) ‘Knee plot’ for the comparison of two reverse transcriptase enzymes (Maxima H Minus versus Superscript IV) in the reverse transcription step of scifi-RNA-seq (the template switching was performed with Maxima H Minus reverse transcriptase in both cases). g) Reads per cell plotted against UMIs per cell to assess the level of sequencing saturation for the two reverse transcriptases. h) Reads per cell plotted against the unique read fraction per cell to assess PCR duplication and library complexity for the two reverse transcriptases. i) Alignment metrics for the two reverse transcriptases.

Article Snippet: Pre-indexed cells or nuclei are pooled and encapsulated into emulsion droplets using a standard microfluidic droplet generator (10x Genomics Chromium).

Techniques: RNA Sequencing, Sequencing, Comparison, Reverse Transcription

Journal: Nature methods

Article Title: Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing

doi: 10.1038/s41592-021-01153-z

Figure Lengend Snippet: a) ‘Knee plot’ showing the number of UMIs (y-axis) per barcode ranked by frequency (x-axis) for scifi-RNA-seq experiments loading different numbers of nuclei. The characteristic inflection points are indicated, which separate nuclei (left, colored lines) from background noise (right, gray lines). To facilitate the comparison between samples, UMIs are normalized to percent of maximum. b) Distribution of the number of nuclei (round1 barcode) per droplet (round2 barcode) when loading different numbers of nuclei. The mean number of nuclei per droplet and nuclei loading concentration per channel are indicated. c) Species-mixing plots showing, for each droplet, the number of reads aligned to the mouse genome (x axis) and human genome (y axis). Transcriptomes were demultiplexed on the basis of the microfluidic round2 barcode alone (left) or on the basis of the combination of round1 and round2 barcodes (right). Dashed lines indicate the expected 1:1 ratio. d) UMIs per cell and fraction of unique readsplotted against the number of nuclei contained in the respective droplet. Box plots depict the interquartile range with marked median and whiskers extending to 1.5 times the interquartile range. The number of droplets that each box plot summarizes is shown on top. e) ‘Knee plot’ for the comparison of scifi-RNA-seq and Chromium profilingusing intact cells, nuclei,or methanol-fixed cells with a standardized loading concentration of 7,500 cells or nuclei per microfluidic channel. f) Dimensionality reduction (UMAP) and clustering (Leiden algorithm)forthe four cell lines. Spurious clusters of doublet cells (gray) are common for Chromium but absent for scifi-RNA-seq. g) Recovery rates for the four cell lines across technologies and cell preparation methods. h) Heatmap showing pairwise correlations and hierarchical clustering for the gene expression profiles across cell lines, cell preparation methods and profiling technologies. i) Dimensionality reduction for aggregated (pseudo-bulk) sample profiles in a large-scale scifi-RNA-seq experiment. j) Dimensionality reduction for 151,788 single-cell transcriptomes, colored by round1 barcodes corresponding to cell lines (left), UMIs per cell (top right) and marker gene expression (bottom right). k) Heatmap showing unfiltered, randomly sampled scifi-RNA-seq profiles for the 100 most specific genes per cell line. l) Gene set enrichment analysis of differentially expressed genes relative to the ARCHS4 database. Closely related cell lines are color coded.

Article Snippet: Pre-indexed cells or nuclei are pooled and encapsulated into emulsion droplets using a standard microfluidic droplet generator (10x Genomics Chromium).

Techniques: RNA Sequencing, Comparison, Concentration Assay, Gene Expression, Marker