Faculty Bibliography

Single-cell RNA sequencing (scRNA-seq) enables the systematic identification of cell populations in a tissue, but characterizing their spatial organization remains challenging. We combine a microarray-based spatial transcriptomics method that reveals spatial patterns of gene expression using an array of spots, each capturing the transcriptomes of multiple adjacent cells, with scRNA-Seq generated from the same sample. To annotate the precise cellular composition of distinct tissue regions, we introduce a method for multimodal intersection analysis. Applying multimodal intersection analysis to primary pancreatic tumors, we find that subpopulations of ductal cells, macrophages, dendritic cells and cancer cells have spatially restricted enrichments, as well as distinct coenrichments with other cell types. Furthermore, we identify colocalization of inflammatory fibroblasts and cancer cells expressing a stress-response gene module. Our approach for mapping the architecture of scRNA-seq-defined subpopulations can be applied to reveal the interactions inherent to complex tissues.

The testis expresses the largest number of genes of any mammalian organ, a finding that has long puzzled molecular biologists. Our single-cell transcriptomic data of human and mouse spermatogenesis provide evidence that this widespread transcription maintains DNA sequence integrity in the male germline by correcting DNA damage through a mechanism we term transcriptional scanning. We find that genes expressed during spermatogenesis display lower mutation rates on the transcribed strand and have low diversity in the population. Moreover, this effect is fine-tuned by the level of gene expression during spermatogenesis. The unexpressed genes, which in our model do not benefit from transcriptional scanning, diverge faster over evolutionary timescales and are enriched for sensory and immune-defense functions. Collectively, we propose that transcriptional scanning shapes germline mutation signatures and modulates mutation rates in a gene-specific manner, maintaining DNA sequence integrity for the bulk of genes but allowing for faster evolution in a specific subset.

Cross-species pathway transplantation enables insight into a biological process not possible through traditional approaches. We replaced the enzymes catalyzing the entire Saccharomyces cerevisiae adenine de novo biosynthesis pathway with the human pathway. While the 'humanized' yeast grew in the absence of adenine, it did so poorly. Dissection of the phenotype revealed that PPAT, the human ortholog of ADE4, showed only partial function whereas all other genes complemented fully. Suppressor analysis revealed other pathways that play a role in adenine de-novo pathway regulation. Phylogenetic analysis pointed to adaptations of enzyme regulation to endogenous metabolite level 'setpoints' in diverse organisms. Using DNA shuffling, we isolated specific amino acids combinations that stabilize the human protein in yeast. Thus, using adenine de novo biosynthesis as a proof of concept, we suggest that the engineering methods used in this study as well as the debugging strategies can be utilized to transplant metabolic pathway from any origin into yeast.

The acceptance of students to a medical school places a considerable emphasis on performance in standardized tests and undergraduate grade point average (uGPA). Traditionally, applicants may be judged as a homogeneous population according to simple quantitative thresholds that implicitly assume a linear relationship between scores and academic success. This 'one-size-fits-all' approach ignores the notion that individuals may show distinct patterns of achievement and follow diverse paths to success. In this study, we examined a dataset composed of 53 variables extracted from the admissions application records of 1,088 students matriculating to NYU School of Medicine between the years 2006-2014. We defined training and test groups and applied K-means clustering to search for distinct groups of applicants. Building an optimized logistic regression model, we then tested the predictive value of this clustering for estimating the success of applicants in medical school, aggregating eight performance measures during the subsequent medical school training as a success factor. We found evidence for four distinct clusters of students-we termed 'signatures'-which differ most substantially according to the absolute level of the applicant's uGPA and its trajectory over the course of undergraduate education. The 'risers' signature showed a relatively higher uGPA and also steeper trajectory; the other signatures showed each remaining combination of these two main factors: 'improvers' relatively lower uGPA, steeper trajectory; 'solids' higher uGPA, flatter trajectory; 'statics' both lower uGPA and flatter trajectory. Examining the success index across signatures, we found that the risers and the statics have significantly higher and lower likelihood of quantifiable success in medical school, respectively. We also found that each signature has a unique set of features that correlate with its success in medical school. The big data approach presented here can more sensitively uncover success potential since it takes into account the inherent heterogeneity within the student population.

While intratumoral heterogeneity has been extensively studied in terms of genetic alterations and phenotypic properties such as drug resistance, only recently has single-cell RNA-seq begun to expose the remarkable transcriptional diversity within tumors. A recent study of glioblastoma by Neftel et al. supports the emerging notion of cancer cell states and demonstrates their plasticity.

Single cell biology is currently revolutionizing developmental and evolutionary biology, revealing new cell types and states in an impressive range of biological systems. With the accumulation of data, however, the field is grappling with a central unanswered question: what exactly is a cell type? This question is further complicated by the inherently dynamic nature of developmental processes. In this Hypothesis article, we propose that a 'periodic table of cell types' can be used as a framework for distinguishing cell types from cell states, in which the periods and groups correspond to developmental trajectories and stages along differentiation, respectively. The different states of the same cell type are further analogous to 'isotopes'. We also highlight how the concept of a periodic table of cell types could be useful for predicting new cell types and states, and for recognizing relationships between cell types throughout development and evolution.