bio-single-cell-splicing

name	bio-single-cell-splicing
description	Analyzes alternative splicing at single-cell resolution. The first decision is library chemistry — 10X 3' is fundamentally limited (RT primes from poly-A, R2 falls in 3' UTR, <0.1 junction read per cell per AS event). Plate-based full-length methods (Smart-seq3, FLASH-seq, VASA-seq, STORM-seq) and single-cell long-read (MAS-Iso-seq, scISOr-Seq2) are the chemistries that give per-cell isoform structure. Tools include MARVEL (R, Smart-seq integrated), BRIE2 (Bayesian PSI with regulatory features and ELBO_gain test), scQuint (junction-cluster, plate-based; not for 10X), SpliZ (annotation-free Z-score), Psix (graph-smoothness regulated AS), and Sierra (alternative polyadenylation, often confused with AS). Use when analyzing isoform usage in scRNA-seq, identifying cell-type-specific splicing, or determining whether scRNA-seq chemistry supports splicing analysis at all.
tool_type	python
primary_tool	MARVEL

Version Compatibility

Reference examples tested with: MARVEL 2.0+, BRIE2 0.2.4+, scQuint 0.1+, SpliZ 0.0.1+, Sierra 1.0+, Psix 0.1+, anndata 0.10+, scanpy 1.10+, pandas 2.2+, scipy 1.13+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Single-Cell Splicing Analysis

The fundamental decision is chemistry, not tool. Most droplet 3' scRNA-seq cannot support transcriptome-wide splicing inference because reverse transcription primes from the poly(A) tail and most reads land in the 3' UTR — far from CDS-region splicing events. Plate-based full-length methods and single-cell long-read sequencing are the chemistries that give per-cell isoform structure across the gene body.

The 10X 3' Problem (Quantified)

Three compounding mechanisms make 10X Chromium 3' (v3.1, GEM-X, v4) hostile to splicing:

1. 3' enrichment: median fragment <1 kb from poly(A); >70% of unique reads fall within 3' UTR.
2. Short R2 (~91 nt): each read straddles at most one junction; usually none, because R2 lands in 3' UTR.
3. PCR concatemers and TSO artifacts: pollute junction detection; UMI collapse is gene-level, not isoform-level.

Quantitative estimate: Only a small fraction of cassette exons sit close enough to the polyA site to be sampled by 3' chemistry (empirical estimates from APA/3'-end atlases — see Tian & Manley 2017 Nat Rev Mol Cell Biol for the 3' UTR isoform landscape). Effective junction read yield from 10X 3' is <0.1 per cell per AS event — vs the 5-10 needed for stable per-cell PSI. Most splicing analyses on 10X 3' data report artifacts.

The 5' kit (10X 5' GEX) does not solve this — it shifts capture from 3' UTR to 5' UTR / TSS-proximal regions. Marginal improvement; not a transcriptome-wide solution. Note that V(D)J recovery requires the 10X Chromium Single Cell Immune Profiling kit (with TCR/BCR-specific enrichment), not 5' GEX alone — postdocs designing immune-repertoire experiments must use the dedicated V(D)J kit.

Decision: Does the Chemistry Support Splicing Analysis?

Chemistry	Splicing analysis viable?	Best alternative if no
10X 3' (Chromium v3, GEM-X, v4, Flex)	No (transcriptome-wide); maybe near-3'-end events	Sierra for APA
10X 5' GEX	Limited; near-5'-end events only	Sierra for alternative TSS; switch to MAS-Iso-seq
Smart-seq2	Yes (full transcript)	MARVEL or BRIE2
Smart-seq3 / Smart-seq3xpress	Yes + UMI molecule counting	MARVEL or BRIE2
FLASH-seq	Yes (faster, cheaper Smart-seq3)	MARVEL or BRIE2
VASA-seq	Yes + total RNA (incl. nascent, IR)	MARVEL with IR analysis
STORM-seq	Yes + total RNA + ribodepletion	MARVEL with IR analysis
MAS-Iso-seq + 10X 5' (PacBio Kinnex)	Yes — full isoforms per cell	FLAMES, scNanoGPS, IsoQuant, see long-read-splicing
scISOr-Seq2 (PacBio + 10X)	Yes — full isoforms with cell-typing	FLAMES, IsoQuant
ONT direct cDNA scRNA	Yes	FLAMES
ONT direct RNA scRNA	Yes + native modifications	FLAMES

Tool Selection Matrix

Tool	Best for	Input	Strengths	Fails when
MARVEL	Smart-seq plate-based and (v2+) 10X droplet unified workflow	Plate or droplet BAMs + Seurat	SE/A5SS/A3SS/MXE/RI/AFE/ALE; modality classification; native Seurat integration; v2 droplet support	R-only
BRIE2	Plate-based with regulatory feature prior	Plate BAM + GFF3 events	Bayesian variational PSI + ELBO_gain test; principled uncertainty; CLI-driven (brie-count, brie-quant)	TensorFlow dependency; slow at scale
scQuint	Plate-based annotation-free junction-cluster quantification (validated on Smart-seq2)	STAR junctions across cells	Cluster-level junction usage; latent Dirichlet	Authors recommend AGAINST use on 10X 3'/5' data (3'-bias confounds); plate-based only
SpliZ	Annotation-free discovery of cell-state-associated splicing	STAR-aligned BAMs	Per-gene Z-score; no event database needed	Annotation-free = power tradeoff
Psix	Regulated AS along trajectories	PSI matrix + kNN graph	Tests graph smoothness; robust to dropout	Needs cell-state graph upstream
Sierra	APA in 10X 3' (NOT splicing)	10X BAM + GTF	Peak-calling 3' ends; DEXSeq DTU on UTR isoforms	APA only; not for cassette exons
pseudobulk leafcutter / rMATS	Between-cell-type differential splicing	Aggregated BAMs	Bulk-level statistical power	Loses within-cluster heterogeneity
MAS-Iso-seq + FLAMES	Full-length single-cell isoforms	10X 5' + PacBio Kinnex	Full isoforms per cell at scale	Cost; complex pipeline

Decision Tree by Goal

Goal	Recommended approach
"Will my 10X 3' data support splicing?"	No transcriptome-wide; consider Sierra for APA. Note: scQuint authors recommend against use on 10X data
Cassette exon analysis in cell types from Smart-seq2	MARVEL with ComputePSI + AssignModality + CompareValues
Discover cell-state-associated splicing without an event database	SpliZ
Test regulated AS along developmental pseudotime	Psix
Per-cell PSI with uncertainty in low-coverage cells	BRIE2
Differential splicing between two well-defined cell types	Pseudobulk leafcutter or rMATS on aggregated BAMs
APA (alternative polyadenylation, often confused with AS)	Sierra
Full-length single-cell isoforms at scale	MAS-Iso-seq + FLAMES (long-read)
Microexons (3-27 nt)	Long-read or aligner with low overhang (uLTRA, deSALT)
snRNA-seq (nuclei) — IR question	Library captures nuclear RNA enriched for incomplete splicing — interpret IR cautiously

MARVEL Plate-Based Workflow

Goal: Run a unified workflow from STAR junctions to cell-type-specific splicing calls.

Approach: Build a wide splice-junction count matrix (rows = junctions keyed by coord.intron, columns = cells), assemble per-event feature tables, then construct MARVEL object with named slots (SpliceJunction, SplicePheno, SpliceFeature, IntronCounts, GeneFeature, Exp, GTF). Quantify PSI per event class, classify modality, test differential splicing.

library(MARVEL); library(Seurat); library(data.table)

seurat_obj <- readRDS('cells.rds')

# Build wide SJ matrix: first column 'coord.intron' (e.g. 'chr1:100007082:100022621'),
# subsequent columns are per-cell sample IDs with junction counts as values.
# This is constructed from STAR SJ.out.tab files (one per cell) merged on intron coord.
sj_files <- list.files('star_pass2/', pattern='SJ.out.tab$', full.names=TRUE)
sj_long <- rbindlist(lapply(sj_files, function(f) {
    d <- fread(f, sep='\t', header=FALSE,
               col.names=c('chr','start','end','strand','motif',
    dcoord.intron  pastedchr dstart dend sep
    dsample  gsub  basenamef
    d .coord.intron sample unique

sj  dcastsj_long coord.intron  sample value.var fill


df.feature.list  
    SE    read.table   header sep
    A5SS  read.table header sep
    A3SS  read.table header sep
    MXE   read.table  header sep
    RI    read.table   header sep



df.pheno  seurat_objmeta.data
df.phenosample.id  rownamesdf.pheno

marvel  CreateMarvelObject
    SpliceJunction  sj
    SplicePheno     df.pheno
    SpliceFeature   df.feature.list
    GeneFeature     read.table header sep
    Exp             read.table header sep row.names
    GTF             rtracklayerimport


marvel  ComputePSImarvel CoverageThreshold EventType
marvel  AssignModalitymarvel EventType
marvel  CompareValues
    marvel
    cell.group.g1  neurons cell.group.g2  glia
    method   n.cells   psi.delta  

For 10X droplet data, MARVEL v2+ provides CreateMarvelObject.10x() and AnnotateSJ.10x() constructors. Verify the exact API via ?CreateMarvelObject.10x in installed MARVEL.

MARVEL classifies events into modalities (Song 2017 Mol Cell): included (PSI1), excluded (PSI0), bimodal (mixture at 0/1), middle (peaked ~0.5), multimodal. Bimodality usually reflects mixed cell states or stochastic monoallelic-like bursting. Mid-modality (peaked at 0.5) can be technical (mixed cells in a droplet) — confirm with full-length data.

BRIE2 Bayesian PSI

Goal: Estimate per-cell PSI with informative regulatory-feature prior; test cell-state association via likelihood-ratio testing on covariate effects.

Approach: BRIE2 is a CLI-driven workflow (brie-count for read counting, brie-quant for variational inference + LRT). Prepare a GFF3 of splicing events, count cell-barcoded junction reads, then fit the model with covariate testing.

# 1. Count splicing events per cell
brie-count \
    -a splicing_events.gff3 \
    -S sample_list.tsv \
    -o brie_counts/ \
    -p 16

# 2. Fit BRIE2 with LRT against the cell-type covariate
brie-quant \
    -i brie_counts/brie_count.h5ad \
    -c cell_metadata.tsv \
    -o brie_quant.h5ad \
    --interceptMode gene \
    --LRTindex All \
    --testBase null \
    --MCsize 3 \
    --batchSize 1000000 \
    -p 16

--interceptMode gene fits a gene-specific intercept (recommended); --LRTindex All tests all covariates; --testBase null uses the null model as the LRT reference. Verify exact flag set via brie-quant -h in installed BRIE2.

import scanpy as sc

adata_splice = sc.read_h5ad('brie_quant.h5ad')
# Per-event covariate effects, ELBO values, and LRT statistics live in
# adata_splice.varm and adata_splice.var; column names depend on BRIE2 version.
# Inspect with: print(adata_splice); print(adata_splice.varm.keys())
# Per-event significance is typically derived from LRT delta-ELBO.

BRIE2 (Huang & Sanguinetti 2021 Genome Biol) uses a sequence-derived feature prior (exon length, GC content, splice site strength, motif counts) to regularize PSI estimates in low-coverage cells. The LRT-based covariate test answers "is this event associated with cell state?" without requiring per-cell PSI accuracy. Threshold the delta-ELBO at ~3 (analogous to log-Bayes-factor); confirm against version-specific output keys via the brie-tutorials repo.

SpliZ for Annotation-Free Discovery

Goal: Identify splicing-defined cell populations without an event database.

Approach: Compute per-gene splicing Z-score across cells; test for cell-state association via permutation.

# SpliZ is a Nextflow pipeline (not a standalone CLI). Configure inputs in a .config
# file (dataname, input_file, libraryType, grouping_level_1/2) - either SICILIAN
# output (SICILIAN=true) or BAMs via a samplesheet CSV + metadata + GTF (SICILIAN=false).
nextflow run salzmanlab/spliz -r main -latest -c spliz.config

SpliZ (Olivieri 2022 Nat Methods) is robust to dropout because it pools junction information across the gene; particularly useful for discovering splicing diversity in heterogeneous tumor samples.

Psix for Regulated AS Along Trajectories

Goal: Detect AS that varies coherently with cell state along a developmental trajectory, robust to dropout.

Approach: Score whether observed PSI is smooth on the cell-cell kNN graph from expression-space embedding.

import psix
import scanpy as sc

adata = sc.read_h5ad('cells.h5ad')
sc.pp.neighbors(adata, n_neighbors=30, use_rep='X_pca')

psix_obj = psix.Psix(adata, psi_matrix_path='psi_matrix.tsv')
psix_obj.run_psix()

regulated = psix_obj.psix_results.query('psix_score > 1.5 and pvalue < 0.05')

Psix (Buen Abad Najar 2022 Genome Res 32:1385) is the principled alternative to imputing PSI: do not impute (it obliterates heterogeneity); test for graph smoothness instead.

Sierra for APA (Not Splicing)

Goal: Detect alternative polyadenylation in 10X 3' data — frequently confounded with AS.

Approach: Peak-call read pile-ups at 3' ends, then DEXSeq-style DTU on 3' UTR isoforms.

library(Sierra)

peak_file <- FindPeaks(
    output.file = 'peaks.txt',
    gtf.file = 'annotation.gtf',
    bam.file = 'possorted_genome_bam.bam'
)

counts <- CountPeaks(
    peak.sites.file = 'peaks.txt',
    gtf.file = 'annotation.gtf',
    bamfile = 'possorted_genome_bam.bam',
    whitelist.file = 'barcodes.tsv'
)

# CountPeaks returns a peak x cell matrix; annotate it and build a peak Seurat
# object before differential-usage testing.
peak.annotations <- AnnotatePeaksFromGTF(
    peak.sites.file = 'peaks.txt',
    gtf.file = 'annotation.gtf',
    output.file = 'peak_annotations.txt'
)

peaks.seurat <- NewPeakSeurat(
    peak.data = counts,
    annot.info = peak.annotations,
    cell.idents = cell_identities
)

apa_results <- DUTestpeaks.seurat population.1  ctrl_cells population.2  trt_cells

If only 10X 3' data is available, this is often what is actually wanted. Distinct UTRs change miRNA targeting, RBP binding, and stability — biologically meaningful but not splicing.

Pseudobulk for Statistical Power

Goal: Recover bulk-level statistical power for differential splicing between cell types.

Approach: Sum junction counts across cells of the same cluster, then run leafcutter / rMATS on aggregated counts.

import pandas as pd
import numpy as np

def pseudobulk_junctions(junction_counts, cell_metadata, groupby='cell_type'):
    out = {}
    for group, cells in cell_metadata.groupby(groupby).groups.items():
        mask = junction_counts.columns.isin(cells)
        out[group] = junction_counts.loc[:, mask].sum(axis=1)
    return pd.DataFrame(out)

Use pseudobulk for differential splicing between well-defined cell types; use per-cell methods for within-population heterogeneity (graded splicing along pseudotime, bimodal cell-state mixtures).

Single-Cell Long-Read = Future of Single-Cell Splicing

In 2024-2026, full-length single-cell long-read sequencing has become practical and is the recommended chemistry for splicing-focused single-cell experiments:

• MAS-Iso-seq / PacBio Kinnex: concatenated full-length cDNA arrays, ~16x throughput vs plain Iso-Seq, compatible with 10X 5' libraries (Al'Khafaji 2024 Nat Biotech)
• scISOr-Seq2: hybrid 10X + PacBio for cell typing + isoform structure (Joglekar et al 2024 Nat Neurosci 27:1051-1063, single-cell long-read brain isoform mapping)
• ONT direct cDNA + 10X: lower cost, similar information content
• FLAMES: barcode demultiplexing + isoform quantification + SNV calling for ONT scRNA (Tian 2021 Genome Biol 22:310)

For splicing-specific full-length single-cell analysis, see long-read-splicing skill.

Per-Tool Failure Modes

MARVEL: SpliceJunction Matrix Format

Trigger: Building the SpliceJunction matrix from STAR SJ.out.tab incorrectly (e.g. long-format instead of wide).

Mechanism: MARVEL plate-based CreateMarvelObject(SpliceJunction = ...) expects a wide matrix with first column coord.intron (formatted chr:start:end) and subsequent columns being per-cell sample IDs with integer junction counts. Long-format data.frames or missing coord.intron column cause runtime errors.

Symptom: "no coord.intron column found" errors; or empty PSI tables despite junction reads being present.

Fix: Verify wide-matrix structure; ensure SJ.out.tabs are merged on the chr:start:end key with cells as columns. Use data.table::dcast for the long->wide reshape.

BRIE2: TensorFlow Memory

Trigger: Large cohort (>10k cells) with deep coverage.

Mechanism: Variational inference loads full count matrix; TensorFlow allocates GPU memory aggressively.

Symptom: OOM kills; training stalls.

Fix: Reduce --batchSize from default (500000) to 100000 or 50000; train per-chromosome batch; use CPU mode for very small cohorts. Note flag is camelCase --batchSize, not --batch_size.

scQuint: 3' Data Sparsity

Trigger: Running scQuint on 10X 3' v3 data hoping for splicing signal.

Mechanism: scQuint's latent Dirichlet model needs junction counts; 10X 3' yields too few junction reads to fit the model robustly.

Symptom: All cells assign to one cluster; no informative splicing signal.

Fix: Pivot to APA analysis with Sierra; or upgrade chemistry to MAS-Iso-seq.

Psix: Missing kNN Graph

Trigger: Running Psix without precomputed cell-cell graph.

Mechanism: Psix tests PSI smoothness on a pre-existing cell-cell graph; without one, no smoothness statistic.

Symptom: Empty results or error about missing connectivities.

Fix: Run sc.pp.neighbors(adata) before Psix; ensure connectivities is in adata.obsp.

Sierra: Annotation Gaps

Trigger: GTF missing 3'UTR annotations.

Mechanism: Sierra peak-calls within annotated 3'UTRs; missing annotations mean missed peaks.

Symptom: Few peaks detected; gene-level coverage but no APA calls.

Fix: Use comprehensive GENCODE annotation; or run de-novo peak calling first.

Reconciliation: When Single-Cell Tools Disagree

Pattern	Likely cause	Action
MARVEL sig, BRIE2 not	Per-cell PSI noise (BRIE2 conservative); MARVEL pseudobulk-like	Trust MARVEL for cell-type comparisons; BRIE2 for within-cluster
BRIE2 sig, MARVEL not	Cell-state effect smoother than cell-type boundary	Test along trajectory with Psix
SpliZ sig, MARVEL not	Annotation-free SpliZ catches novel events	Investigate junction structure manually
Sierra sig, MARVEL not	Sierra is APA, MARVEL is splicing — different biology	Distinguish in interpretation
Pseudobulk sig, per-cell not	Power issue; effect averaged out per-cell	Report at cluster level, not per-cell

Quantitative Concepts Unique to Single-Cell

Per-cell PSI vs pseudobulk PSI:

• Per-cell PSI: meaningful only when junction coverage exceeds ~10-20 reads per cell per event (plate-based or long-read).
• Pseudobulk PSI: aggregate, recovers bulk-level statistical power, discards within-cluster heterogeneity.

Modality detection in PSI distributions (Song 2017 Mol Cell):

Modality	PSI distribution	Biology
Included	Peaked at 1	Constitutive inclusion
Excluded	Peaked at 0	Constitutive skipping
Bimodal	Mixture at 0 and 1	Mixed cell states or monoallelic-like bursting
Middle	Peaked ~0.5	Often technical (well-contamination, doublets, or low-coverage shrinkage to prior); confirm with full-length
Multimodal	Multiple peaks	Complex regulation; deserves follow-up

Beta-binomial vs binomial models: with sparse counts, binomial PSI is overdispersed. Beta-binomial models (BRIE2; leafcutter2 as Dirichlet-multinomial cluster-level) handle this. For very sparse droplet data, even beta-binomial fits poorly per cell — collapse to pseudobulk.

Imputation pitfalls: naive imputation (MAGIC, scImpute, ALRA) of expression matrices is not appropriate for PSI: imputing missing junction counts averages over neighboring cells and obliterates the very heterogeneity under study. Psix's approach — testing smoothness of observed PSI on the kNN graph — is the principled alternative.

Cell-Type-Specific Splicing Biology

System	Event	Regulator
Neural microexons	3-27 nt exons enriched in brain	SRRM4/nSR100 (Irimia 2014 Cell); SRRM3 in retina/photoreceptors (Ciampi 2022 PNAS)
Neural differentiation	PTBP1 -> PTBP2 switch	miR-124 represses PTBP1; derepresses neural exons (Boutz 2007 Genes Dev)
T-cell activation	CD45 RA -> RO	hnRNP-L, ESRP-mediated
Erythropoiesis	EPB41 exon 16	Splicing factor switching during maturation
Cardiac development	TTN N2BA -> N2B	MBNL1/CELF1 antagonism
EMT	FGFR2 IIIb -> IIIc, ENAH exon 11a	ESRP1/2 loss in mesenchymal state (Warzecha 2009 Mol Cell)
Activated T cell	CD45 isoform shift	Multiple SR/hnRNP regulators

Quality Thresholds

Metric	Recommendation
Cells per event with reads	>=50 (per-cell PSI); >=200 cells per cluster (pseudobulk)
Junction reads per event per cell	>=5 with coverage; <=1 = unreliable
PSI variance for cell-type call	<0.1 within cluster, >0.2 between clusters
Library	full-length plate or long-read for transcriptome-wide; 3' for APA only
Doublet filtering	Required before splicing analysis (DoubletFinder, Scrublet)
Cells per cluster (pseudobulk)	>=100 ideal; >=50 minimum
nuclear vs whole-cell	snRNA-seq enriches IR; treat with caution

Common Errors

Error	Cause	Solution
MARVEL: ComputePSI returns empty	STAR SJ.out.tab missing strand info	Re-run STAR with --outSJtype Standard
brie.tl.fit: NaN loss	Insufficient junction reads per cell	Filter cells with min_reads=20; raise threshold
scQuint: convergence not reached	LDA model fit on too-few junctions	Aggregate by chromosome; or switch chemistry
Psix: missing connectivities	Neighbors graph not computed	Run sc.pp.neighbors(adata) first
Sierra: no peaks called	GTF missing 3'UTR annotations	Use comprehensive GENCODE; or de-novo peak-call
MARVEL: ggplot error	Seurat version mismatch	Match MARVEL and Seurat versions
FLAMES: barcode rescue failed	Short-read 10X output not in expected directory	Verify cellranger output structure

Common Pitfalls

• Treating 10X 3' splicing analysis as legitimate — the chemistry doesn't support it. Use Sierra for APA or upgrade to MAS-Iso-seq.
• Imputing PSI matrices — destroys the heterogeneity to be detected. Use Psix or BRIE2 instead.
• Per-cell PSI on droplet data — typically too sparse for stable estimates. Use pseudobulk first, then drill down to per-cell.
• Confusing APA with splicing — Sierra results look like AS but are 3' UTR isoforms. Different machinery, different biology.
• snRNA-seq IR signal misinterpreted as splicing dysregulation — nuclear RNA is enriched for incompletely spliced transcripts; baseline IR is high.
• Trusting per-cell PSI from BRIE2 without ELBO_gain test — BRIE2's per-cell point estimates are noisy; the principled output is the ELBO_gain cell-state-association statistic.
• Microexon analysis with default short-read aligners — anchors >=20 nt miss most microexons; use VAST-TOOLS, MicroExonator, or long-read.
• Skipping doublet filtering before splicing — doublets create artificial PSI mid-modality.

Related Skills

• single-cell/preprocessing - QC and normalization (must run before splicing)
• single-cell/clustering - Cell type annotation prerequisite
• single-cell/doublet-detection - Doublet filtering critical for splicing
• single-cell/data-io - h5ad / Seurat I/O
• splicing-quantification - Bulk RNA-seq comparison context
• long-read-splicing - Full-isoform analysis from MAS-Iso-seq, scISOr-Seq2; future of single-cell splicing

References

• Huang & Sanguinetti 2021 Genome Biol - BRIE2
• Wen et al 2023 Nucleic Acids Research 51:e29 - MARVEL
• Benegas, Fischer & Song 2022 eLife - scQuint (annotation-free single-cell splicing analysis, validated on Smart-seq2)
• Olivieri et al 2022 Nat Methods - SpliZ
• Buen Abad Najar et al 2022 Genome Research 32:1385 - Psix
• Patrick et al 2020 Genome Biol - Sierra
• Song et al 2017 Mol Cell - splicing modality classification
• Picelli et al 2014 Nat Protoc - Smart-seq2
• Hagemann-Jensen et al 2020 Nat Biotech - Smart-seq3
• Hagemann-Jensen et al 2022 Nat Biotech - Smart-seq3xpress
• Hahaut et al 2022 Nat Biotech - FLASH-seq
• Salmen et al 2022 Nat Biotech - VASA-seq
• Johnson et al 2022 bioRxiv 10.1101/2022.03.14.484332 - STORM-seq (preprint)
• Al'Khafaji et al 2024 Nat Biotech - MAS-Iso-seq / Kinnex
• Tian et al 2021 Genome Biology 22:310 - FLAMES
• Joglekar et al 2024 Nat Neurosci 27:1051-1063 - scISOr-Seq2 single-cell brain isoform mapping
• Irimia et al 2014 Cell - neural microexons / SRRM4
• Ciampi et al 2022 PNAS 119:e2117090119 - SRRM3-dependent photoreceptor microexons
• Boutz et al 2007 Genes Dev - PTBP1/PTBP2 neural switch
• Tian & Manley 2017 Nat Rev Mol Cell Biol - alternative polyadenylation and 3' UTR isoforms