The immune system is critical in modulating cancer progression, but knowledge of immune composition, phenotype, and interactions with tumor is limited. We used multiplexed ion beam imaging by time-of-flight (MIBI-TOF) to simultaneously quantify in situ expression of 36 proteins covering identity, function, and immune regulation at sub-cellular resolution in 41 triple-negative breast cancer patients. Multi-step processing, including deep-learning-based segmentation, revealed variability in the composition of tumor-immune populations across individuals, reconciled by overall immune infiltration and enriched co-occurrence of immune subpopulations and checkpoint expression. Spatial enrichment analysis showed immune mixed and compartmentalized tumors, coinciding with expression of PD1, PD-L1, and IDO in a cell-type- and location-specific manner. Ordered immune structures along the tumor-immune border were associated with compartmentalization and linked to survival. These data demonstrate organization in the tumor-immune microenvironment that is structured in cellular composition, spatial arrangement, and regulatory-protein expression and provide a framework to apply multiplexed imaging to immune oncology.

Despite much research, our understanding of the rules by which cis-regulatory sequences are translated into expression levels is still lacking. We devised a method for obtaining parallel and highly accurate expression measurements of thousands of fully designed promoters, and applied it to measure the effect of systematic changes to location, number, orientation, affinity and organization of transcription factor (TF) binding sites and of nucleosome disfavoring sequences. Our analyses reveal a clear relationship between expression and binding site number, and TF-specific dependencies of expression on the distance between sites and gene starts including a striking ~10bp periodic relationship. We also demonstrate the utility of our approach for measuring TF sequence specificities and sensitivity of TF sites to surrounding sequence context, and for profiling the activity of most yeast transcription factors. Our method is readily applicable for studying both the cis and trans effects of genotype on transcriptional, post-transcriptional, and translational control.

A major challenge in the analysis of tissue imaging data is cell segmentation, the task of identifying the precise boundary of every cell in an image. To address this problem we constructed TissueNet, a dataset for training segmentation models that contains more than 1 million manually labeled cells, an order of magnitude more than all previously published segmentation training datasets. We used TissueNet to train Mesmer, a deep learning-enabled segmentation algorithm. We demonstrated that Mesmer is more accurate than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We then adapted Mesmer to harness cell lineage information in highly multiplexed datasets and used this enhanced version to quantify cell morphology changes during human gestation. All code, data, and models are released as a community resource.

CRISPR/Cas is a recently discovered prokaryotic immune system, which 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 consequence of autoimmunity rather than gene regulation. We propose that accidental incorporation of self nucleic-acids by CRISPR can incur an autoimmune fitness cost, which may explain the abundance of degraded CRISPR systems across prokaryotes. CRISPR/Cas, an acquired anti-viral system in prokaryotesClustered regularly interspaced short palindromic repeats (CRISPR) loci are found in nearly all of archaeal and about 40% of sequenced bacterial genomes. CRISPR loci, together with their associated cas genes, have recently been shown to constitute a defense system that restricts propagation of incoming viruses and plasmids [1,2]. CRISPR arrays are composed of short repeat sequences separated by similarly sized hyper-variable "spacer" sequences, flanked on one side by an AT-rich sequence called the leader. The discovery that CRISPR spacers often match DNA from foreign elements led to the realization that they represent a "memory of past genetic aggressions" [3][4][5].Step by step, the mechanism underlying CRISPR defense has begun to unravel, yet our understanding of this system is far from complete. It has been revealed that the CRISPR locus is transcribed into a single RNA transcript, which is then further cleaved by the Cas proteins to generate smaller CRISPR RNA (crRNA) units, each including one targeting spacer [6]. These units then interfere with the incoming foreign genetic material by complementary base-pairing with the foreign nucleic acids [7][8][9][10]. CRISPR systems have been divided into different clusters based on their repeat sequences [11], which correlate with different subtypes of cas genes [12]. cas subtypes mtube, ecoli and nmeni were shown to likely target DNA [2,5,6,13], whereas the cas module ramp was shown to target RNA [14].© 2010 Elsevier Ltd. All rights reserved. * Corresponding author: rotem.sorek@weizmann.ac.il. † These authors contributed equally Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Although CRISPR/Cas was initially prophesized to be ...

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 μm × 800 μm 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.