About

Sandrine Dudoit is a Professor of Statistics at the School of Public Health, within the Division of Epidemiology and Biostatistics. Her research and teaching activities focus on the development and application of statistical methods and software for analyzing biomedical and genomic data. Her work encompasses statistical methodology, applications to biomedical and genomic research, and statistical computing. Dudoit's research interests include biostatistics, gene expression and regulation, genomics and genetics, as well as machine learning and algorithms. She is involved in mentoring designated emphasis students and collaborates on projects related to biomedical data analysis, contributing to advancements in statistical approaches for understanding complex biological systems.

Research topics

• Biology
• Data Mining
• Medicine
• Artificial Intelligence
• Computer Science
• Computational biology
• Genetics
• Neuroscience
• Algorithm
• Statistics
• Virology
• Cartography
• Pathology
• Environmental health
• Anatomy
• Geography
• Mathematics

Selected publications

• A multimodal cell census and atlas of the mammalian primary motor cortex

  Nature · 2021 · 564 citations

  • Neuroscience
  • Biology
  • Computational biology

  . First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.

  DOI

• Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmia

  Science Advances · 2020 · 1234 citations

  • Biology
  • Genetics
  • Virology

  Altered olfactory function is a common symptom of COVID-19, but its etiology is unknown. A key question is whether SARS-CoV-2 (CoV-2) - the causal agent in COVID-19 - affects olfaction directly, by infecting olfactory sensory neurons or their targets in the olfactory bulb, or indirectly, through perturbation of supporting cells. Here we identify cell types in the olfactory epithelium and olfactory bulb that express SARS-CoV-2 cell entry molecules. Bulk sequencing demonstrated that mouse, non-human primate and human olfactory mucosa expresses two key genes involved in CoV-2 entry, ACE2 and TMPRSS2. However, single cell sequencing revealed that ACE2 is expressed in support cells, stem cells, and perivascular cells, rather than in neurons. Immunostaining confirmed these results and revealed pervasive expression of ACE2 protein in dorsally-located olfactory epithelial sustentacular cells and olfactory bulb pericytes in the mouse. These findings suggest that CoV-2 infection of non-neuronal cell types leads to anosmia and related disturbances in odor perception in COVID-19 patients.

  DOI

• Trajectory-based differential expression analysis for single-cell sequencing data

  Nature Communications · 2020 · 835 citations

  • Computer Science
  • Data Mining
  • Computer Science

  Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the trajectory, or (ii) differentially expressed between lineages, to illuminate the underlying biological processes. Current data analysis procedures, however, either fail to exploit the continuous resolution provided by trajectory inference, or fail to pinpoint the exact types of differential expression. We introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression. By incorporating observation-level weights, the model additionally allows to account for zero inflation. We evaluate the method on simulated datasets and on real datasets from droplet-based and full-length protocols, and show that it yields biological insights through a clear interpretation of the data.

  DOI

Recent grants

• NIH Grant R01LM007609

  NIH · $1.2M · 2006

• Programs of Gene Expression in Olfactory Neurogenesis

  NIH · $6.6M · 2004–2024

Frequent coauthors

• Alain Barrier

  Metabolism and Renal Physiology

  89 shared

• Antoinette Lemoine

  Assistance Publique – Hôpitaux de Paris

  68 shared

• Piérre-Yves Boëlle

  Sorbonne Université

  59 shared

• Davide Risso

  University of Padua

  49 shared

• Mark J. van der Laan

  44 shared

• Stephen M. Rappaport

  43 shared

• Antoine Flahault

  42 shared

• Hasmik Grigoryan

  Biofuel Research Team

  38 shared

Labs

• Sandrine DudoitPI

Similar researchers at University of California, Berkeley

• Peidong Yangh-262
• Jennifer A. Doudnah-230
• Jeffrey R. Longh-189
• John F. Hartwigh-180
• Rasmus Nielsenh-180