About

Stephen Quake is the Lee Otterson Professor of Bioengineering and Applied Physics at Stanford University and an Investigator at the Howard Hughes Medical Institute. His research interests lie at the intersection of physics, biology, and biotechnology. Over the past half decade, he has focused on understanding the basic physics and biological applications of microfluidic technology. His group pioneered the development of Microfluidic Large Scale Integration (LSI), demonstrating the first integrated microfluidic devices with thousands of mechanical valves. This technology is helping to pave the way for large scale automation of biology at the nanoliter scale, and he and his students have been exploring applications of 'lab on a chip' technology in functional genomics, genetic analysis, and protein design. Throughout his career, Quake has also been active in the field of single molecule biophysics; he has focused on precision measurements on single molecules, and in 2003 his group demonstrated the first successful single molecule DNA sequencing experiments.

Research topics

• Biology
• Genetics
• Computer Science
• Computational biology
• Cell biology
• Artificial Intelligence
• Medicine
• Immunology
• Pathology
• Data Mining
• Internal medicine
• Virology
• Microbiology
• Bioinformatics
• Evolutionary biology
• Operating system
• Anatomy
• Database
• Pediatrics
• Obstetrics
• Risk analysis (engineering)
• Data science

Selected publications

• Cell types of origin of the cell-free transcriptome

  Nature Biotechnology · 2022 · 99 citations

  • Biology
  • Computational biology
  • Genetics

  Cell-free RNA from liquid biopsies can be analyzed to determine disease tissue of origin. We extend this concept to identify cell types of origin using the Tabula Sapiens transcriptomic cell atlas as well as individual tissue transcriptomic cell atlases in combination with the Human Protein Atlas RNA consensus dataset. We define cell type signature scores, which allow the inference of cell types that contribute to cell-free RNA for a variety of diseases.

  DOI

• Fly Cell Atlas: A single-nucleus transcriptomic atlas of the adult fruit fly

  Science · 2022 · 834 citations

  • Computer Science
  • Biology
  • Anatomy

  community and serves as a reference to study genetic perturbations and disease models at single-cell resolution.

  PublisherOA PDFDOI

• The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans

  Science · 2022 · 979 citations

  • Biology
  • Computational biology
  • Cell biology

  Molecular characterization of cell types using single-cell transcriptome sequencing is revolutionizing cell biology and enabling new insights into the physiology of human organs. We created a human reference atlas comprising nearly 500,000 cells from 24 different tissues and organs, many from the same donor. This atlas enabled molecular characterization of more than 400 cell types, their distribution across tissues, and tissue-specific variation in gene expression. Using multiple tissues from a single donor enabled identification of the clonal distribution of T cells between tissues, identification of the tissue-specific mutation rate in B cells, and analysis of the cell cycle state and proliferative potential of shared cell types across tissues. Cell type-specific RNA splicing was discovered and analyzed across tissues within an individual.

  PublisherOA PDFDOI

• Adversarial domain translation networks for integrating large-scale atlas-level single-cell datasets

  Nature Computational Science · 2022 · 47 citations

  • Computer Science
  • Computer Science
  • Artificial Intelligence
  PublisherDOI

• Quantifying rapid bacterial evolution and transmission within the mouse intestine

  Cell Host & Microbe · 2021 · 51 citations

  • Biology
  • Genetics
  • Microbiology
  DOI

• Chronic lung diseases are associated with gene expression programs favoring SARS-CoV-2 entry and severity

  Nature Communications · 2021 · 43 citations

  • Immunology
  • Biology
  • Medicine

  Patients with chronic lung disease (CLD) have an increased risk for severe coronavirus disease-19 (COVID-19) and poor outcomes. Here, we analyze the transcriptomes of 611,398 single cells isolated from healthy and CLD lungs to identify molecular characteristics of lung cells that may account for worse COVID-19 outcomes in patients with chronic lung diseases. We observe a similar cellular distribution and relative expression of SARS-CoV-2 entry factors in control and CLD lungs. CLD AT2 cells express higher levels of genes linked directly to the efficiency of viral replication and the innate immune response. Additionally, we identify basal differences in inflammatory gene expression programs that highlight how CLD alters the inflammatory microenvironment encountered upon viral exposure to the peripheral lung. Our study indicates that CLD is accompanied by changes in cell-type-specific gene expression programs that prime the lung epithelium for and influence the innate and adaptive immune responses to SARS-CoV-2 infection.

  DOI

• Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics

  Nature Medicine · 2021 · 372 citations

  • Biology
  • Immunology
  • Virology

  cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial-macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention.

  PublisherOA PDFDOI

• Multiomics Characterization of Preterm Birth in Low- and Middle-Income Countries

  JAMA Network Open · 2020 · 103 citations

  • Medicine
  • Obstetrics
  • Pediatrics

  Importance: Worldwide, preterm birth (PTB) is the single largest cause of deaths in the perinatal and neonatal period and is associated with increased morbidity in young children. The cause of PTB is multifactorial, and the development of generalizable biological models may enable early detection and guide therapeutic studies. Objective: To investigate the ability of transcriptomics and proteomics profiling of plasma and metabolomics analysis of urine to identify early biological measurements associated with PTB. Design, Setting, and Participants: This diagnostic/prognostic study analyzed plasma and urine samples collected from May 2014 to June 2017 from pregnant women in 5 biorepository cohorts in low- and middle-income countries (LMICs; ie, Matlab, Bangladesh; Lusaka, Zambia; Sylhet, Bangladesh; Karachi, Pakistan; and Pemba, Tanzania). These cohorts were established to study maternal and fetal outcomes and were supported by the Alliance for Maternal and Newborn Health Improvement and the Global Alliance to Prevent Prematurity and Stillbirth biorepositories. Data were analyzed from December 2018 to July 2019. Exposures: Blood and urine specimens that were collected early during pregnancy (median sampling time of 13.6 weeks of gestation, according to ultrasonography) were processed, stored, and shipped to the laboratories under uniform protocols. Plasma samples were assayed for targeted measurement of proteins and untargeted cell-free ribonucleic acid profiling; urine samples were assayed for metabolites. Main Outcomes and Measures: The PTB phenotype was defined as the delivery of a live infant before completing 37 weeks of gestation. Results: Of the 81 pregnant women included in this study, 39 had PTBs (48.1%) and 42 had term pregnancies (51.9%) (mean [SD] age of 24.8 [5.3] years). Univariate analysis demonstrated functional biological differences across the 5 cohorts. A cohort-adjusted machine learning algorithm was applied to each biological data set, and then a higher-level machine learning modeling combined the results into a final integrative model. The integrated model was more accurate, with an area under the receiver operating characteristic curve (AUROC) of 0.83 (95% CI, 0.72-0.91) compared with the models derived for each independent biological modality (transcriptomics AUROC, 0.73 [95% CI, 0.61-0.83]; metabolomics AUROC, 0.59 [95% CI, 0.47-0.72]; and proteomics AUROC, 0.75 [95% CI, 0.64-0.85]). Primary features associated with PTB included an inflammatory module as well as a metabolomic module measured in urine associated with the glutamine and glutamate metabolism and valine, leucine, and isoleucine biosynthesis pathways. Conclusions and Relevance: This study found that, in LMICs and high PTB settings, major biological adaptations during term pregnancy follow a generalizable model and the predictive accuracy for PTB was augmented by combining various omics data sets, suggesting that PTB is a condition that manifests within multiple biological systems. These data sets, with machine learning partnerships, may be a key step in developing valuable predictive tests and intervention candidates for preventing PTB.

  DOI

• A molecular cell atlas of the human lung from single-cell RNA sequencing

  Nature · 2020 · 1797 citations

  • Biology
  • Computational biology
  • Cell biology
  DOI

• Enabling Technologies for Personalized and Precision Medicine

  Trends in biotechnology · 2020 · 465 citations

  • Computer Science
  • Artificial Intelligence
  • Computer Science
  DOI

Recent grants

• Administrative Core

  NIH · $92.6M · 2003–2029

• NIH Grant DP1OD000251

  NIH · $3.9M · 2010

• NIH Grant R29HG001642

  NIH · $594k · 2002

• NIH Grant RC4AI092673

  NIH · $2.5M · 2013

• Linking islet cell function and identity from in vitro to in situ

  NIH · $3.0M · 2018–2023

Frequent coauthors

• Norma Neff

  Chan Zuckerberg Initiative (United States)

  177 shared

• Liqun Luo

  Howard Hughes Medical Institute

  150 shared

• Spyros Darmanis

  Genomic Health (United States)

  117 shared

• Irving L. Weissman

  Stanford University

  107 shared

• Fabio Zanini

  100 shared

• Tony Wyss‐Coray

  Stanford Medicine

  98 shared

• Robert C. Jones

  Louisiana State University in Shreveport

  98 shared

• Hongjie Li

  Baylor College of Medicine

  78 shared

Labs

• Quake LabPI

Education

• Ph.D., Physics

  Stanford University

  1993

• B.S., Physics

  Harvard University

  1988

Similar researchers at Stanford University

• Yi Cuih-287
• Zhenan Baoh-224
• Karl Deisserothh-218
• Michael Snyderh-215
• Roger Blandfordh-205