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Research map/Stanford University/Stephen Quake Lab

Research group guide

Stephen Quake Lab

Stephen Quake

Research develops biological measurement tools and applies them to single-cell genomics, genomic diagnostics, microbial ecology, and quantitative biological analysis. This independent primer distinguishes historical lab contributions, collaborative and consortium results, current research, and clinical screening from diagnosis.

Official research website ↗
B-cell repertoireBioengineeringCell atlasesCell-free DNACell-free RNADeconvolutionDNA sequencingGenomic diagnosticsGenomicsImmune repertoire sequencingLiquid biopsyMetagenomicsMicrobial ecologyMicrobiomeMicrofluidicsMolecular countingNoninvasive prenatal testingPrenatal genomicsQuantitative biologyRNA sequencingSingle-cell genomicsSingle-cell RNA sequencingSingle-molecule measurementSystems biologyT-cell receptor repertoireTabula SapiensTranscriptomicsTransplant monitoring

This independent educational primer introduces scientific concepts relevant to research themes in the Stephen Quake Lab at Stanford University. It is not an official Stanford University or Stephen Quake Lab course and does not imply endorsement or affiliation.

Questions behind the work

Research questions

01

How do microfluidics and individual-molecule measurements reveal information hidden in averages?

02

How do single-cell RNA profiles distinguish cell types and states?

03

What can a sampled human cell atlas reveal across tissues?

04

How do cell-free DNA fragments act as molecular counters?

05

How can reference signatures help infer contributors to a cell-free RNA mixture?

06

What do immune clones and microbial sequences reveal, and what additional evidence is required?

Your recommended path

Learn this research

Research primer

Measuring Life: From Single Cells to Liquid Biopsies

6 lessons · ~29 minutes

New measurement tools reveal individual cells, human cell atlases, molecular traces in blood, immune clones, and microbial ecosystems. A video-first introduction to genomics and bioengineering.

Watch videos
  1. 01Why better measurements change biology
  2. 02Single-cell genomics: why one cell at a time matters
  3. 03Building a map of the human body, one cell at a time
  4. 04Liquid biopsy: reading the body's molecular debris
  5. 05Cell-free RNA: can a blood sample tell us what tissues are doing?
  6. 06From immune systems to microbiomes: what else can sequencing reveal?

Key concepts

Cell-free DNA and liquid biopsy

Interpret fragmented extracellular DNA as a molecular mixture.

Cell-free RNA and tissue-of-origin inference

Explain why cfRNA can carry expression information but source inference needs assumptions.

Genome versus transcriptome and bulk versus single-cell measurement

Distinguish genome, transcriptome and bulk versus single-cell information.

Human cell atlases and reference transcriptomes

Use cell-atlas reference profiles while accounting for sampling and capture bias.

Immune repertoire sequencing and clonal dynamics

Interpret immune clone counts without inferring antigen or complete tissue coverage.

Measurement technology, scale, and microfluidics

Explain how microfluidic scale, isolation and valves change what can be measured.

Metagenomics and quantitative microbial ecology

Separate microbial DNA detection from viability, infection and causal ecology.

Molecular counting in prenatal and transplant monitoring

Relate counting noise, placental DNA and donor DNA to scoped screening and monitoring principles.

Reference atlases, mixture models, and deconvolution

Describe reference-based mixture deconvolution, validation and identifiability limits.

Single-cell RNA sequencing, cell types, and cell states

Interpret RNA matrices, cell types and states without equating clusters with biological identities.

Single-molecule measurement and measurement-driven discovery

Distinguish individual-molecule measurements from averages and sole-invention claims.

Tabula Sapiens and cross-tissue cellular diversity

Explain the consortium atlas and cross-tissue diversity within its donor and tissue scope.

Important papers

Science · 2002

Microfluidic large-scale integration

Thorsen T, Maerkl SJ, Quake SR

Why this matters: HISTORICAL QUAKE-LAB PRIMARY RESULT: integrated chambers and valves automate many measurements.

DOI: 10.1126/science.1076996

Nature · 2022

Early prediction of preeclampsia in pregnancy with cell-free RNA

Moufarrej MN, Vorperian SK, Wong RJ, et al., Stevenson DK, Quake SR

Why this matters: RESEARCH VALIDATION: 404 samples from 199 mothers; an 18-gene panel at 5–16 weeks with independent validation. Possible test basis, not universal clinical readiness.

DOI: 10.1038/s41586-022-04410-z

Nat Biotechnol · 2022

Cell types of origin of the cell-free transcriptome

Vorperian SK, Moufarrej MN, Tabula Sapiens Consortium, Quake SR

Why this matters: CONSORTIUM / COLLABORATIVE PRIMARY RESULT: Tabula Sapiens reference-based nu-SVR validated on bulk tissues. Similar and missing signatures limit identifiability.

DOI: 10.1038/s41587-021-01188-9

Independent project ideas inspired by this research

Projects you could do

Educational ideas using public or synthetic data. These projects are not offered or supervised by the lab or research group.

Intermediate

Compare bulk and single-cell signals

Computational simulation or public-data analysis

Simulate two cell populations with equal bulk averages but different single-cell expression.

Background
Relevant primer lessons, Basic Python and probability
Data
{"Synthetic RNA-count matrices."}
Output
Plots comparing averaging and distribution, with explicit capture noise.

Independent learning idea, not offered or supervised by the lab. Simulations or public non-identifying data only; no wet-lab work, clinical samples, genetic modification, culturing, patient diagnosis, or private human genomic records.

Intermediate

Interpret public single-cell clusters

Computational simulation or public-data analysis

Compare expression clusters with published cell-type annotations and state markers.

Background
Relevant primer lessons, Basic Python and probability
Data
{"Public non-identifying reference expression matrices and aggregate annotations."}
Output
A reproducible cluster analysis with marker evidence and type-versus-state caveats.

Independent learning idea, not offered or supervised by the lab. Simulations or public non-identifying data only; no wet-lab work, clinical samples, genetic modification, culturing, patient diagnosis, or private human genomic records.

Intermediate

Evaluate a reference tissue classifier

Computational simulation or public-data analysis

Train on public reference profiles, holding out donors for evaluation.

Background
Relevant primer lessons, Basic Python and probability
Data
{"Public non-identifying reference atlas expression matrices."}
Output
Held-out confusion matrix and an uncertainty report for missing or overlapping signatures.

Independent learning idea, not offered or supervised by the lab. Simulations or public non-identifying data only; no wet-lab work, clinical samples, genetic modification, culturing, patient diagnosis, or private human genomic records.

Intermediate

Simulate cfDNA counting uncertainty

Computational simulation or public-data analysis

Vary independent fragment counts and simulate sampling noise without clinical predictions.

Background
Relevant primer lessons, Basic Python and probability
Data
{"Fully synthetic fragment counts and stated mixture proportions."}
Output
Noise-versus-count plots compared with 1/sqrt(N), including bias limitations.

Independent learning idea, not offered or supervised by the lab. Simulations or public non-identifying data only; no wet-lab work, clinical samples, genetic modification, culturing, patient diagnosis, or private human genomic records.

Intermediate

Analyze microbial sequence mixtures

Computational simulation or public-data analysis

Classify public microbial reference sequences in simulated mixtures and contamination controls.

Background
Relevant primer lessons, Basic Python and probability
Data
{"Public reference genomes and synthetic nonclinical mixtures."}
Output
A classification/contamination report distinguishing sequence detection, viability and causation.

Independent learning idea, not offered or supervised by the lab. Simulations or public non-identifying data only; no wet-lab work, clinical samples, genetic modification, culturing, patient diagnosis, or private human genomic records.

Related labs and groups

Ranked by shared research topics in the current collection.

California Institute of Technology

Rob Phillips Physical Biology Laboratory

Rob Phillips

Research using quantitative experiments, statistical mechanics, information theory, nonequilibrium physics, and large-scale biological measurements to discover predictive principles in gene regulation, biological fidelity, active matter, and viral ecology.

Active matterAllosteryBacteriophagesBiological fidelityBiological numeracy

Shared topics: Metagenomics, Quantitative biology, Systems biology

6 lessons · ~29 minutes

Explore the lab