Cost of single cell RNA sequencing

Why do you do this experiment?

Long-read RNA sequencing enables the identification and quantification of RNA expressed in a cell or a sample (the transcriptome) at the isoform resolution.

Input 100-10M cells

Output Fastq file (100M-25B PE reads) -> Single cell gene expression

Strategic Value

• Characterise cell type and cell state in a complex sample (developing embryo, tumor sample or simply a healthy tissue).
• Massive parallel perturbation with CROPseq, PerturbSeq or pooled cell culture.

Cost & Scale

• Variable per run: \$2700 for 20k cells, \$190 (96 cells) - \$36,500 (1M cells)
• Cost breakdown:
  • Cell barcoding of RNA: \$90 (96 cells) - \$10k (1M cells)
  • Sequencing: \$100 (10M, 1Gb) - \$16,500 (25B reads, 7.5Tb)
• Capex: Magnetic Stand 96 (\$800), Thermocycler (\$10-20k), TapeStation (\$6-30k), Chromium Controller (\$20k, needed for 10x Genomics only), Illumina NovaseqX or MGI T20 (\$1M)

Experimental Modules

1. Cell barcoding of RNA (8h, 4h hands-on)
2. Sequencing library preparation (2h15, 30’ hands-on)
3. Sequencing run (48h, 30’ hands-on)

Ops & Throughput

Turnaround: 4+ days (day 1 single cell RNA barcoding, day 2 library prep, day 3 or later sequencing >40h)

Hands-on time: 5h

Parallelizability: High. All steps can be done in parallel for as many samples as needed.

Bottlenecks: availability of sequencer (2-4 flowcells per sequencer fully occupied).

Batching: 1 preparation per technician, number of samples up to 96 depending on the protocol multiplexing possibilities.

Automation readiness: Partial. Custom solution via automation specialists for Parse Bioscience and Scale Bioscience. Partially released Chromium Connect by 10x Genomics. Worth mentionning is the Cellen One X1 Neo which can easily be adapted for SmartSeq2 automation.

Outsourceability: Yes, most CROs offer it.

Data scale: 100M-25B reads/sample, 1Gb-7.5Tb/sample

Data API

Raw format: FASTQ

Processed format: sparse count matrix -> cell type (with RNA velocity if relevant)

Resolution: 3’-biased polyA gene products expression for individual cells

Analysis Ecosystem

1. QC and cleaning
   • fastqc: Quality control of the run
   • cutadapt: Trimming of sequencing adapters from the reads
2. Alignement and cell barcode attribution pipelines (most use STAR under the hood):
   • CellRanger for 10x Genomics
   • splitpipe for Parse Bioscience
   • smartseq2 pipeline
3. (optional) RNA velocity
   • scvelo for RNA velocity
4. Count processing and cell clustering
   • scanpy in python, faster and better suited for large dataset (>100k)
   • Seurat in R
5. Cell type annotation (many tools exist, including foundation models)
   • ScType: Marker based annotation
   • Single cell foundation models such as scBERT, Geneformer, scGPT, CellFM, or xTrimoscFoundation.
6. Differential expression
   • glmgampoi: Fast gamma-poisson distribution fitting for single cell data.
   • DESeq2 or PyDESeq2
   • edgeR or edgePy

Public datasets

• Human Cell Atlas (HCA): Aggregation of single cell sequencing data from human samples, covers >400 tissues from healthy and disease samples.
• Single Cell Atlas (SCA): A single-cell multi-omics atlas presenting comprehensive overview sacross 125 healthy adult and fetal tissues.
• Single Cell Expression Atlas: Aggregation of single cell sequencing data across multipe organisms.
• Tabula Sapiens: A first-draft human cell atlas of over 1.1M cells from 28 organs of 24 normal human subjects
• Broad Institute Single Cell portal: Millions of cell from hundred of studies across multiple organisms and modalities
• Genotype-Tissue Expression (GTEx): Single cell data of 8 major organs from a subset of individuals
• Tahoe100M: 100M cells across 50 cancer cell lines perturbed with 1,100 small-molecule single perturbations
• scBaseCount: An AI agent-curated, uniformly processed, and continually expanding single cell data repository of human tissues by the Arc Institute
• Gene Expression Omnibus (GEO): Repository of sequencing data from publications
• European Nucleotide Archive (ENA): Repository of sequencing data from publications

Pitfalls & Failure Modes

• Ambient RNA contamination is a prime noise factor in single cell RNA-seq. Ambiant RNA is release by dead cells when they loose membrane integrity and can be barcoded with cells barcode. This problem is most present with encapsulation methods such as 10x or
• Most protocols rely on polyA oligos to barcode the RNAs, leading to only mRNA and lncRNA being captured. Parse Bioscience Evercode takes an intermediate route with a mix of polyA and random hexamers and 10x offers capture sequence on their beads. If you are interested mainly in non-polyA transcripts at the single cell resolution, there are protocols but they are usually lower throughput.
• polyA capture followed by fragmentation induces a 3’ bias, limiting the resolution to the gene level. SmartSeq2 notably uses tagmentation to insert barcodes, providing reads covering the full length of the transcript. A protocol variation with 10x to perform long read sequencing strongly decreases the 3’ bias for short transcripts (<10kb) but requires using long read sequencing technologies. Takara also provides a long read variation of SmartSeq.

Related publications

• Picelli2014: SmartSeq2 foundational paper
• Rosenberg2018: Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding (Parse Bioscience foundational paper)
• Zheng2017: Massively parallel digital transcriptional profiling of single cells (10x genomics foundational paper)
• Gaisser2024: High-throughput single-cell transcriptomics of bacteria using combinatorial barcoding
• Pan2024: Single Cell Atlas: a single-cell multi-omics human cell encyclopedia
• Heimberg2024: A cell atlas foundation model for scalable search of similar human cells
• Peidli2024: scPerturb: harmonized single-cell perturbation data
• Replogle2022: Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq
• Bergen2020: Generalizing RNA velocity to transient cell states through dynamical modeling
• Clarke2021: Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods
• Ianevski2022: Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data
• Fu2024: A comparison of scRNA-seq annotation methods based on experimentally labeled immune cell subtype dataset

Order list

Single well barcoding: 1 - 384 cells (SmartSeq2 or FLASHseq)

Droplet-based barcoding: 100 - 100k cells (10x genomics)

Split-pool barcoding: 100k - 10M cells (Parse Bioscience or Scale Bioscience)

Protocol variations

• CROPseq/PerturbSeq: Perturb cells with CRISPR technologies and read which guide RNA is present in each single cell, either via a polyA-sgRNA (Datlinger2017) or a dedicated capture sequence (Dixit2016, Replogle2020)
• Single cell RNAseq with long read sequencing. This usually simply requires a longer RT step to produce a full cDNA copy of the transcript, skipping cDNA fragmentation and using a long read technology.
• Demultiplexing via SNPs with Souporcell enables the multiplexing or an arbitrary number of samples of different genetic origin (patient or cell line). <!–
• Single cell methylation can be obtained with bisulfite transformation of the barcoded DNA. –>

Bonus: Main metrics for common kits

_{^{This post is part of a series on the cost of experiments. All costs are orders of magnitude and are susceptible to have changed between the post and your order date. All costs assume you perform the whole pipeline in house and do not include labor costs. For outsourcing a decent first estimate is to double the indicated costs. Cheap consumables are not always included if they affect less than 1% of the cost. Always check the protocols coming with the kits for the complete list of consumables to order.}}