Here’s a brief history of the Yanai Lab, where our research revolves around the study of biological processes through the lens of global gene expression.

1. Prior to establishing the lab, Itai worked on comparative genomics (PhD work) and comparative transcriptomics (Postdoc work). In the field of gene regulation, he proposed that gene expression programs evolve in large part by neutral processes, and he studied this using gene expression comparisons between human and mouse, five nematodes, and two amphibians. Key publications are:

Yanai, I., Benjamin H., Shmoish, M., Chalifa-Caspi, V., Shklar, M., Ophir, R., Bar-Even, A. Horn-Saban, S., Safran, M., Domany, E., Lancet, D., Shmueli, O. Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification. Bioinformatics 21, 650-659 (2005).

Yanai, I., Derti, A. & DeLisi, C. Genes linked by fusion events are generally of the same functional category: A systematic analysis of 30 microbial genomes. Proceedings of the National Academy of Sciences of the United States of America. 98, 7940-5 (2001).

Yanai, I. & DeLisi, C. The society of genes: networks of functional links between genes from comparative genomics. Genome Biology. 3, 1-15 (2002).

Yanai, I., Graur, D. & Ophir, R. Incongruent expression profiles between human and mouse orthologous genes suggest widespread neutral evolution of transcription control. OMICS. 8, 15–24 (2004).

Yanai, I., Korbel, J., Boue, S., Bork, P., & Lercher M. J. Similar gene expression profiles do not imply similar tissue functions. Trends in Genetics. 22, 132–8 (2006).

Yanai, I. & Hunter, C. P. Comparison of diverse developmental transcriptomes reveals that coexpression of gene neighbors is not evolutionarily conserved. Genome Research. 19, 2214–20 (2009).

Yanai, I., Peshkin, L., Jorgensen, P. & Kirschner, M. Mapping gene expression in two Xenopus species: evolutionary constraints and developmental flexibility. Developmental Cell. 20, 483-496 (2011).

2. In 2012, our lab published CEL-Seq, one of the first methods for single-cell transcriptomics (scRNA-Seq). CEL-Seq is recognized as the most sensitive and robust such method and as having ignited the single-cell revolution. CEL-Seq uses linear amplification by in vitro transcription, and the latest CEL-Seq2 protocol has increased sensitivity and reproducibility.

Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: Single-cell RNA-Seq by multiplexed linear amplification. Cell Reports. 2, 666-673 (2012).

Hashimshony, T., Senderovich, N., Avital, G., Klochendler, A., de Leeuw, Y., Anavy, L., Gennert, D., Li, S., Livak, K. J., Rozenblatt-Rosen, O., Dor, Y., Regev, A. & Yanai, I. CEL-Seq2: Sensitive highly-multiplexed single-cell RNA-Seq. Genome Biology. 17:77 (2016).

3. Evolution of Development: Also while the lab was at the Technion – Israel Institute of Technology, we used systematic gene expression analyses to provide evidence that developmental milestones punctuate gene expression and proposed that ventral enclosure is the nematode phylotypic stage. We identified the time and germ-layer of expression of all genes throughout C. elegans embryogenesis and used this information to infer the evolutionary history of the endoderm germ layer. Studying ten phyla at the transcriptome level, we also revealed a universal mid-developmental transition during the embryogenesis of each species which we believe has implications for the study of animal body plans.

Hashimshony, T., Feder, M., Levin, M., Hall, B. K. & Yanai, I. Spatiotemporal transcriptomics reveals the evolutionary history of the endoderm germ layer. Nature. 519, 219–222 (2015).

Levin, M., Anavy, L., Cole, A. G., Winter, E., Mostov, N., Khair, S., Senderovich, N., Kovalev, E., Silver, D. H., Feder, M., Fernandez-Valverde, S. L., Nakanishi, N., Simmons, D., Simakov, O., Larsson, T., Liu, S., Jerafi-Vider, A., Yaniv, K., Ryan, J. F., Martindale, M. Q.,  Rink, J., Arendt, D., Degnan, S. M., Degnan, B. M., Hashimshony, T. & Yanai I. The mid-developmental transition and the evolution of animal body plans. Nature. 531, 637–641 (2016).

Zalts, H. & Yanai, I. Developmental constraints shape the evolution of the nematode mid-developmental transition. Nature Ecology & Evolution. 1, 102–107 (2017).

4. Alternative splicing. As a postdoc Itai and  colleagues provided evidence for the notion that alternative splicing and gene duplications are interchangeable evolutionary mechanisms. Ten years later, graduate student Vlad Grishkevich and me elucidated that relationship through the lens of gene length and expression levels.

Kopelman, N. M., Lancet, D. & Yanai, I. Alternative splicing and gene duplication are inversely correlated evolutionary mechanisms. Nature Genetics. 37, 588–589 (2005).

Grishkevich, V. & Yanai, I. Gene length and expression level shape genomic novelties. Genome Research. 24, 1497-503 (2014).

5. Evolutionary systems biology of cell states. Using single-cell RNA-Seq we were led to propose a new mechanism – which we termed ‘transcriptional scanning’ – that makes sense of the previously unexplained expression of a majority of genes in the testes; it acts to reduce the germline mutation rate while enabling a select set of genes to diverge faster over evolutionary time-scales to facilitate rapid adaptations. Also using scRNA-Seq we characterized the cell types and cell states of the pancres.

Xia B., Yan Y., Baron M., Wagner F., Barkley, D., Chiodin, M., Kim S.Y., Keefe D.L., Alukal J.P., Boeke J.D., & Yanai I. Widespread transcriptional scanning in the testis modulates gene evolution rates. Cell. 2020 180: 248-262.

Baron, M., Veres, A., Wolock, SL., Faust, A. L., Gaujoux, R., Vetere, A., Ryu, J. H., Wagner, B. K., Shen-Orr, S., Klein, A. M., Melton, D. A. & Yanai I. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Systems. 3, 346-360 (2016).

6. Host-pathogen interactions. Our lab published scDual-Seq to simultaneously analyze the transcriptomes of both a host and an intra-cellular bacterial pathogen at the single-cell level; this was previously believed to be impossible.

Avital, G., Avraham, R., Fan, A., Hashimshony, T., Hung, D. T. & Yanai, I. scDual-Seq: Mapping the gene regulatory program of Salmonella infection by host and pathogen single-cell RNA-sequencing. Genome Biology. 18:200, (2017).

7. Cancer cell state research. We have been exploring cancer using single-cell RNA-Seq. We have revealed cell states in melanoma and provided compelling evidence of a population of cells exhibiting a stress phenotype using intact human tissues and a transgenic zebrafish model of melanoma. We demonstrated that this population is highly enriched in cancer versus normal cells, using spatial transcriptomics as well as immunofluorescence on intact tissues. We have made the provocative hypothesis that cells with the stress program would be resistant to clinically relevant drugs in melanoma.

Baron M, Tagore M, Hunter MV, Kim IS, Moncada R, Yan Y, Campbell NRWhite RM, Yanai I. The stress-like cancer cell state is a consistent component of tumorigenesis. Cell Systems. 2020 Nov 18; 11(5):536-546.e7. PMID: 32910905.

8. Spatial transcriptomics. We have been early proponents of spatial transcriptomics, developing algorithms to mapping cell-cell interactions.

Rao A, Barkley D, Franca GS, Yanai I. Exploring tissue architecture using spatial transcriptomics. Nature 2021 596: 211-220 [Review].

Moncada R, Barkley D, Wagner F, Chiodin M, Devlin JC, Baron M, Hajdu CH, Simeone D, Yanai I. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nature Biotechnology. 2020 38: 333-342.

Hunter MV., Moncada R, Weiss JM, Yanai I, White RM. Spatially resolved transcriptomics reveals the architecture of the tumor/microenvironment interface. bioRxiv. 2021:2020.11.05.368753. (under revision at Nature Communications).

9. Computational methods. An important requirement for our approach is the concomitant development of computational and experimental tools across data modalities. Computationally, we develop many of the methods that we not only use ourselves, but that are adopted widely in the community, such as MIA, ELOPER, BLIND, ZAVIT, and TSI.

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