The immune system plays a critical role in modulating cancer progression. However, knowledge of the composition, phenotype, organization, and interactions between immune cells and tumor cells is limited. Leeat applies multiplexed imaging to study the interplay between the tumor and the immune system. She develops computational tools that allow to tease various layers of information from rich multiplexed-imaging data and employ them to infer design principles in tumor-immune interactions.
Honors & Awards
Potdoctoral Fellowship, Damon Runyon Cancer Research Foundation (2017-2021)
Israel's National Award Program for Advancing Women in Science, Weizmann Institute of Science (2016)
Postdoctoral Fellowship, Rothschild Foundation (2016)
Postdoctoral Fellowship, Fulbright Foundation (2016)
Master of Science, Weizmann Institute Of Science (2010)
Doctor of Philosophy, Weizmann Institute Of Science (2016)
Bachelor of Science, Tel-Aviv University (2008)
Robert Angelo, Postdoctoral Faculty Sponsor
Modeling Multiplexed Images with Spatial-LDA Reveals Novel Tissue Microenvironments.
Journal of computational biology : a journal of computational molecular cell biology
Recent in situ multiplexed profiling techniques provide insight into microenvironment formation, maintenance, and transformation through a lens of localized cellular phenotype distribution. In this article, we introduce a method for recovering signatures of microenvironments from such data. We use topic models to identify characteristic cell types overrepresented in neighborhoods that serve as proxies for microenvironment. Furthermore, by assuming spatial coherence among neighboring microenvironments our model limits the number of parameters that need to be learned and permits data-driven decisions about the size of cellular neighborhoods. We apply this method to uncover anatomically known structures in mouse spleen-identifying distinct population of spleen B cells that are defined by their characteristic neighborhoods. Next, we apply the method to a dataset of triple-negative breast cancer tumors from 41 patients to study the structure of tumor-immune boundary. We uncover previously reported tumor-immune microenvironment near the tumor-immune boundary enriched for immune cells with high Indoleamine-pyrrole 2,3-dioxygenase (IDO) and Programmed death ligand 1 (PD-L1) and a novel, immunosuppressed, microenvironment-enriched for cells expressing CD45 and FoxP3.
View details for DOI 10.1089/cmb.2019.0340
View details for PubMedID 32243203
Combining Multiplexed Ion Beam Imaging (MIBI) with Convolutional Neural Networks to accurately segment cells in human tissue
View details for Web of Science ID 000496473200232
MIBI-TOF: A multiplexed imaging platform relates cellular phenotypes and tissue structure.
2019; 5 (10): eaax5851
Understanding tissue structure and function requires tools that quantify the expression of multiple proteins while preserving spatial information. Here, we describe MIBI-TOF (multiplexed ion beam imaging by time of flight), an instrument that uses bright ion sources and orthogonal time-of-flight mass spectrometry to image metal-tagged antibodies at subcellular resolution in clinical tissue sections. We demonstrate quantitative, full periodic table coverage across a five-log dynamic range, imaging 36 labeled antibodies simultaneously with histochemical stains and endogenous elements. We image fields of view up to 800 mum * 800 mum at resolutions down to 260 nm with sensitivities approaching single-molecule detection. We leverage these properties to interrogate intrapatient heterogeneity in tumor organization in triple-negative breast cancer, revealing regional variability in tumor cell phenotypes in contrast to a structured immune response. Given its versatility and sample back-compatibility, MIBI-TOF is positioned to leverage existing annotated, archival tissue cohorts to explore emerging questions in cancer, immunology, and neurobiology.
View details for DOI 10.1126/sciadv.aax5851
View details for PubMedID 31633026
- Comprehensive characterization of human decidual immune cells involvement in spiral artery remodelling MOSBY-ELSEVIER. 2019: S27–S28
- A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging CELL 2018; 174 (6): 1373-+
- A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging Cell 2018; 174 (6): 1373-87.e19
Massively Parallel Interrogation of the Effects of Gene Expression Levels on Fitness
2016; 166 (5): 1282-+
Data of gene expression levels across individuals, cell types, and disease states is expanding, yet our understanding of how expression levels impact phenotype is limited. Here, we present a massively parallel system for assaying the effect of gene expression levels on fitness in Saccharomyces cerevisiae by systematically altering the expression level of ∼100 genes at ∼100 distinct levels spanning a 500-fold range at high resolution. We show that the relationship between expression levels and growth is gene and environment specific and provides information on the function, stoichiometry, and interactions of genes. Wild-type expression levels in some conditions are not optimal for growth, and genes whose fitness is greatly affected by small changes in expression level tend to exhibit lower cell-to-cell variability in expression. Our study addresses a fundamental gap in understanding the functional significance of gene expression regulation and offers a framework for evaluating the phenotypic effects of expression variation.
View details for DOI 10.1016/j.cell.2016.07.024
View details for Web of Science ID 000382259500023
View details for PubMedID 27545349
Noise in gene expression is coupled to growth rate
2015; 25 (12): 1893–1902
Genetically identical cells exposed to the same environment display variability in gene expression (noise), with important consequences for the fidelity of cellular regulation and biological function. Although population average gene expression is tightly coupled to growth rate, the effects of changes in environmental conditions on expression variability are not known. Here, we measure the single-cell expression distributions of approximately 900 Saccharomyces cerevisiae promoters across four environmental conditions using flow cytometry, and find that gene expression noise is tightly coupled to the environment and is generally higher at lower growth rates. Nutrient-poor conditions, which support lower growth rates, display elevated levels of noise for most promoters, regardless of their specific expression values. We present a simple model of noise in expression that results from having an asynchronous population, with cells at different cell-cycle stages, and with different partitioning of the cells between the stages at different growth rates. This model predicts non-monotonic global changes in noise at different growth rates as well as overall higher variability in expression for cell-cycle-regulated genes in all conditions. The consistency between this model and our data, as well as with noise measurements of cells growing in a chemostat at well-defined growth rates, suggests that cell-cycle heterogeneity is a major contributor to gene expression noise. Finally, we identify gene and promoter features that play a role in gene expression noise across conditions. Our results show the existence of growth-related global changes in gene expression noise and suggest their potential phenotypic implications.
View details for DOI 10.1101/gr.191635.115
View details for Web of Science ID 000365830400011
View details for PubMedID 26355006
View details for PubMedCentralID PMC4665010
Probing the effect of promoters on noise in gene expression using thousands of designed sequences
2014; 24 (10): 1698–1706
Genetically identical cells exhibit large variability (noise) in gene expression, with important consequences for cellular function. Although the amount of noise decreases with and is thus partly determined by the mean expression level, the extent to which different promoter sequences can deviate away from this trend is not fully known. Here, we present a high-throughput method for measuring promoter-driven noise for thousands of designed synthetic promoters in parallel. We use it to investigate how promoters encode different noise levels and find that the noise levels of promoters with similar mean expression levels can vary more than one order of magnitude, with nucleosome-disfavoring sequences resulting in lower noise and more transcription factor binding sites resulting in higher noise. We propose a kinetic model of gene expression that takes into account the nonspecific DNA binding and one-dimensional sliding along the DNA, which occurs when transcription factors search for their target sites. We show that this assumption can improve the prediction of the mean-independent component of expression noise for our designed promoter sequences, suggesting that a transcription factor target search may affect gene expression noise. Consistent with our findings in designed promoters, we find that binding-site multiplicity in native promoters is associated with higher expression noise. Overall, our results demonstrate that small changes in promoter DNA sequence can tune noise levels in a manner that is predictable and partly decoupled from effects on the mean expression levels. These insights may assist in designing promoters with desired noise levels.
View details for DOI 10.1101/gr.168773.113
View details for Web of Science ID 000342542800013
View details for PubMedID 25030889
View details for PubMedCentralID PMC4199362
Promoters maintain their relative activity levels under different growth conditions
MOLECULAR SYSTEMS BIOLOGY
2013; 9: 701
Most genes change expression levels across conditions, but it is unclear which of these changes represents specific regulation and what determines their quantitative degree. Here, we accurately measured activities of ~900 S. cerevisiae and ~1800 E. coli promoters using fluorescent reporters. We show that in both organisms 60-90% of promoters change their expression between conditions by a constant global scaling factor that depends only on the conditions and not on the promoter's identity. Quantifying such global effects allows precise characterization of specific regulation-promoters deviating from the global scale line. These are organized into few functionally related groups that also adhere to scale lines and preserve their relative activities across conditions. Thus, only several scaling factors suffice to accurately describe genome-wide expression profiles across conditions. We present a parameter-free passive resource allocation model that quantitatively accounts for the global scaling factors. It suggests that many changes in expression across conditions result from global effects and not specific regulation, and provides means for quantitative interpretation of expression profiles.
View details for DOI 10.1038/msb.2013.59
View details for Web of Science ID 000326733900011
View details for PubMedID 24169404
View details for PubMedCentralID PMC3817408
Sequence features of yeast and human core promoters that are predictive of maximal promoter activity
NUCLEIC ACIDS RESEARCH
2013; 41 (11): 5569–81
The core promoter is the region in which RNA polymerase II is recruited to the DNA and acts to initiate transcription, but the extent to which the core promoter sequence determines promoter activity levels is largely unknown. Here, we identified several base content and k-mer sequence features of the yeast core promoter sequence that are highly predictive of maximal promoter activity. These features are mainly located in the region 75 bp upstream and 50 bp downstream of the main transcription start site, and their associations hold for both constitutively active promoters and promoters that are induced or repressed in specific conditions. Our results unravel several architectural features of yeast core promoters and suggest that the yeast core promoter sequence downstream of the TATA box (or of similar sequences involved in recruitment of the pre-initiation complex) is a major determinant of maximal promoter activity. We further show that human core promoters also contain features that are indicative of maximal promoter activity; thus, our results emphasize the important role of the core promoter sequence in transcriptional regulation.
View details for DOI 10.1093/nar/gkt256
View details for Web of Science ID 000320116200011
View details for PubMedID 23599004
View details for PubMedCentralID PMC3675475
Manipulating nucleosome disfavoring sequences allows fine-tune regulation of gene expression in yeast
2012; 44 (7): 743-U163
Understanding how precise control of gene expression is specified within regulatory DNA sequences is a key challenge with far-reaching implications. Many studies have focused on the regulatory role of transcription factor-binding sites. Here, we explore the transcriptional effects of different elements, nucleosome-disfavoring sequences and, specifically, poly(dA:dT) tracts that are highly prevalent in eukaryotic promoters. By measuring promoter activity for a large-scale promoter library, designed with systematic manipulations to the properties and spatial arrangement of poly(dA:dT) tracts, we show that these tracts significantly and causally affect transcription. We show that manipulating these elements offers a general genetic mechanism, applicable to promoters regulated by different transcription factors, for tuning expression in a predictable manner, with resolution that can be even finer than that attained by altering transcription factor sites. Overall, our results advance the understanding of the regulatory code and suggest a potential mechanism by which promoters yielding prespecified expression patterns can be designed.
View details for DOI 10.1038/ng.2305
View details for Web of Science ID 000305886900006
View details for PubMedID 22634752
Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters
2012; 30 (6): 521-?
Despite extensive research, our understanding of the rules according to which cis-regulatory sequences are converted into gene expression is limited. We devised a method for obtaining parallel, highly accurate gene expression measurements from thousands of designed promoters and applied it to measure the effect of systematic changes in the location, number, orientation, affinity and organization of transcription-factor binding sites and nucleosome-disfavoring sequences. Our analyses reveal a clear relationship between expression and binding-site multiplicity, as well as dependencies of expression on the distance between transcription-factor binding sites and gene starts which are transcription-factor specific, including a striking ∼10-bp periodic relationship between gene expression and binding-site location. We show how this approach can measure transcription-factor sequence specificities and the sensitivity of transcription-factor sites to the surrounding sequence context, and compare the activity of 75 yeast transcription factors. Our method can be used to study both cis and trans effects of genotype on transcriptional, post-transcriptional and translational control.
View details for DOI 10.1038/nbt.2205
View details for Web of Science ID 000305158600022
View details for PubMedID 22609971
View details for PubMedCentralID PMC3374032
Compensation for differences in gene copy number among yeast ribosomal proteins is encoded within their promoters
2011; 21 (12): 2114-2128
Coordinate regulation of ribosomal protein (RP) genes is key for controlling cell growth. In yeast, it is unclear how this regulation achieves the required equimolar amounts of the different RP components, given that some RP genes exist in duplicate copies, while others have only one copy. Here, we tested whether the solution to this challenge is partly encoded within the DNA sequence of the RP promoters, by fusing 110 different RP promoters to a fluorescent gene reporter, allowing us to robustly detect differences in their promoter activities that are as small as ~10%. We found that single-copy RP promoters have significantly higher activities, suggesting that proper RP stoichiometry is indeed partly encoded within the RP promoters. Notably, we also partially uncovered how this regulation is encoded by finding that RP promoters with higher activity have more nucleosome-disfavoring sequences and characteristic spatial organizations of these sequences and of binding sites for key RP regulators. Mutations in these elements result in a significant decrease of RP promoter activity. Thus, our results suggest that intrinsic (DNA-dependent) nucleosome organization may be a key mechanism by which genomes encode biologically meaningful promoter activities. Our approach can readily be applied to uncover how transcriptional programs of other promoters are encoded.
View details for DOI 10.1101/gr.119669.110
View details for Web of Science ID 000297918600012
View details for PubMedID 22009988
View details for PubMedCentralID PMC3227101
Self-targeting by CRISPR: gene regulation or autoimmunity?
TRENDS IN GENETICS
2010; 26 (8): 335–40
The recently discovered prokaryotic immune system known as CRISPR (clustered regularly interspaced short palindromic repeats) is based on small RNAs ('spacers') that restrict phage and plasmid infection. It has been hypothesized that CRISPRs can also regulate self gene expression by utilizing spacers that target self genes. By analyzing CRISPRs from 330 organisms we found that one in every 250 spacers is self-targeting, and that such self-targeting occurs in 18% of all CRISPR-bearing organisms. However, complete lack of conservation across species, combined with abundance of degraded repeats near self-targeting spacers, suggests that self-targeting is a form of autoimmunity rather than a regulatory mechanism. We propose that accidental incorporation of self nucleic acids by CRISPR can incur an autoimmune fitness cost, and this could explain the abundance of degraded CRISPR systems across prokaryotes.
View details for DOI 10.1016/j.tig.2010.05.008
View details for Web of Science ID 000280903800001
View details for PubMedID 20598393
View details for PubMedCentralID PMC2910793