Browsing by Author "Regev, Aviv"
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Item Open Access A Switch in p53 Dynamics Marks Cells That Escape from DSB-Induced Cell Cycle Arrest.(Cell reports, 2020-08) Tsabar, Michael; Mock, Caroline S; Venkatachalam, Veena; Reyes, Jose; Karhohs, Kyle W; Oliver, Trudy G; Regev, Aviv; Jambhekar, Ashwini; Lahav, GalitCellular responses to stimuli can evolve over time, resulting in distinct early and late phases in response to a single signal. DNA damage induces a complex response that is largely orchestrated by the transcription factor p53, whose dynamics influence whether a damaged cell will arrest and repair the damage or will initiate cell death. How p53 responses and cellular outcomes evolve in the presence of continuous DNA damage remains unknown. Here, we have found that a subset of cells switches from oscillating to sustained p53 dynamics several days after undergoing damage. The switch results from cell cycle progression in the presence of damaged DNA, which activates the caspase-2-PIDDosome, a complex that stabilizes p53 by inactivating its negative regulator MDM2. This work defines a molecular pathway that is activated if the canonical checkpoints fail to halt mitosis in the presence of damaged DNA.Item Open Access COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets.(Nature, 2021-07) Delorey, Toni M; Ziegler, Carly GK; Heimberg, Graham; Normand, Rachelly; Yang, Yiming; Segerstolpe, Åsa; Abbondanza, Domenic; Fleming, Stephen J; Subramanian, Ayshwarya; Montoro, Daniel T; Jagadeesh, Karthik A; Dey, Kushal K; Sen, Pritha; Slyper, Michal; Pita-Juárez, Yered H; Phillips, Devan; Biermann, Jana; Bloom-Ackermann, Zohar; Barkas, Nikolaos; Ganna, Andrea; Gomez, James; Melms, Johannes C; Katsyv, Igor; Normandin, Erica; Naderi, Pourya; Popov, Yury V; Raju, Siddharth S; Niezen, Sebastian; Tsai, Linus T-Y; Siddle, Katherine J; Sud, Malika; Tran, Victoria M; Vellarikkal, Shamsudheen K; Wang, Yiping; Amir-Zilberstein, Liat; Atri, Deepak S; Beechem, Joseph; Brook, Olga R; Chen, Jonathan; Divakar, Prajan; Dorceus, Phylicia; Engreitz, Jesse M; Essene, Adam; Fitzgerald, Donna M; Fropf, Robin; Gazal, Steven; Gould, Joshua; Grzyb, John; Harvey, Tyler; Hecht, Jonathan; Hether, Tyler; Jané-Valbuena, Judit; Leney-Greene, Michael; Ma, Hui; McCabe, Cristin; McLoughlin, Daniel E; Miller, Eric M; Muus, Christoph; Niemi, Mari; Padera, Robert; Pan, Liuliu; Pant, Deepti; Pe'er, Carmel; Pfiffner-Borges, Jenna; Pinto, Christopher J; Plaisted, Jacob; Reeves, Jason; Ross, Marty; Rudy, Melissa; Rueckert, Erroll H; Siciliano, Michelle; Sturm, Alexander; Todres, Ellen; Waghray, Avinash; Warren, Sarah; Zhang, Shuting; Zollinger, Daniel R; Cosimi, Lisa; Gupta, Rajat M; Hacohen, Nir; Hibshoosh, Hanina; Hide, Winston; Price, Alkes L; Rajagopal, Jayaraj; Tata, Purushothama Rao; Riedel, Stefan; Szabo, Gyongyi; Tickle, Timothy L; Ellinor, Patrick T; Hung, Deborah; Sabeti, Pardis C; Novak, Richard; Rogers, Robert; Ingber, Donald E; Jiang, Z Gordon; Juric, Dejan; Babadi, Mehrtash; Farhi, Samouil L; Izar, Benjamin; Stone, James R; Vlachos, Ioannis S; Solomon, Isaac H; Ashenberg, Orr; Porter, Caroline BM; Li, Bo; Shalek, Alex K; Villani, Alexandra-Chloé; Rozenblatt-Rosen, Orit; Regev, AvivCOVID-19, which is caused by SARS-CoV-2, can result in acute respiratory distress syndrome and multiple organ failure1-4, but little is known about its pathophysiology. Here we generated single-cell atlases of 24 lung, 16 kidney, 16 liver and 19 heart autopsy tissue samples and spatial atlases of 14 lung samples from donors who died of COVID-19. Integrated computational analysis uncovered substantial remodelling in the lung epithelial, immune and stromal compartments, with evidence of multiple paths of failed tissue regeneration, including defective alveolar type 2 differentiation and expansion of fibroblasts and putative TP63+ intrapulmonary basal-like progenitor cells. Viral RNAs were enriched in mononuclear phagocytic and endothelial lung cells, which induced specific host programs. Spatial analysis in lung distinguished inflammatory host responses in lung regions with and without viral RNA. Analysis of the other tissue atlases showed transcriptional alterations in multiple cell types in heart tissue from donors with COVID-19, and mapped cell types and genes implicated with disease severity based on COVID-19 genome-wide association studies. Our foundational dataset elucidates the biological effect of severe SARS-CoV-2 infection across the body, a key step towards new treatments.Item Open Access The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution.(Cell, 2020-04) Rozenblatt-Rosen, Orit; Regev, Aviv; Oberdoerffer, Philipp; Nawy, Tal; Hupalowska, Anna; Rood, Jennifer E; Ashenberg, Orr; Cerami, Ethan; Coffey, Robert J; Demir, Emek; Ding, Li; Esplin, Edward D; Ford, James M; Goecks, Jeremy; Ghosh, Sharmistha; Gray, Joe W; Guinney, Justin; Hanlon, Sean E; Hughes, Shannon K; Hwang, E Shelley; Iacobuzio-Donahue, Christine A; Jané-Valbuena, Judit; Johnson, Bruce E; Lau, Ken S; Lively, Tracy; Mazzilli, Sarah A; Pe'er, Dana; Santagata, Sandro; Shalek, Alex K; Schapiro, Denis; Snyder, Michael P; Sorger, Peter K; Spira, Avrum E; Srivastava, Sudhir; Tan, Kai; West, Robert B; Williams, Elizabeth H; Human Tumor Atlas NetworkCrucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.