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3. Mouse fetal growth restriction through parental and fetal immune gene variation and intercellular communications cascade

Author

Kaur, Gurman, Porter, Caroline B. M., Ashenberg, Orr, Lee, Jack, Riesenfeld, Samantha J., Hofree, Matan, Aggelakopoulou, Maria, Subramanian, Ayshwarya, Kuttikkatte, Subita Balaram, Attfield, Kathrine E., Desel, Christiane A. E., Davies, Jessica L., Evans, Hayley G., Avraham-Davidi, Inbal, Nguyen, Lan T., Dionne, Danielle A., Neumann, Anna E., Jensen, Lise Torp, Barber, Thomas R., Soilleux, Elizabeth, Carrington, Mary, McVean, Gil, Rozenblatt-Rosen, Orit, Regev, Aviv, and Fugger, Lars

Published
2022
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4. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram

Author

Biancalani, Tommaso, Scalia, Gabriele, Buffoni, Lorenzo, Avasthi, Raghav, Lu, Ziqing, Sanger, Aman, Tokcan, Neriman, Vanderburg, Charles R., Segerstolpe, Åsa, Zhang, Meng, Avraham-Davidi, Inbal, Vickovic, Sanja, Nitzan, Mor, Ma, Sai, Subramanian, Ayshwarya, Lipinski, Michal, Buenrostro, Jason, Brown, Nik Bear, Fanelli, Duccio, Zhuang, Xiaowei, Macosko, Evan Z., and Regev, Aviv

Published
2021
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5. Massively parallel single-nucleus RNA-seq with DroNc-seq

Author

Habib, Naomi, Avraham-Davidi, Inbal, Basu, Anindita, Burks, Tyler, Shekhar, Karthik, Hofree, Matan, Choudhury, Sourav R, Aguet, François, Gelfand, Ellen, Ardlie, Kristin, Weitz, David A, Rozenblatt-Rosen, Orit, Zhang, Feng, and Regev, Aviv

Subjects
3T3 Cells, Animals, Biomarkers, HEK293 Cells, Humans, Mice, Principal Component Analysis, RNA, Sequence Analysis, RNA, Single-Cell Analysis, Transcription, Genetic, Biological Sciences, Technology, Medical and Health Sciences, Developmental Biology
Abstract

Single-nucleus RNA sequencing (sNuc-seq) profiles RNA from tissues that are preserved or cannot be dissociated, but it does not provide high throughput. Here, we develop DroNc-seq: massively parallel sNuc-seq with droplet technology. We profile 39,111 nuclei from mouse and human archived brain samples to demonstrate sensitive, efficient, and unbiased classification of cell types, paving the way for systematic charting of cell atlases.

Published
2017

7. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram

Author

Massachusetts Institute of Technology. Department of Biology, Biancalani, Tommaso, Scalia, Gabriele, Buffoni, Lorenzo, Avasthi, Raghav, Lu, Ziqing, Sanger, Aman, Tokcan, Neriman, Vanderburg, Charles R, Segerstolpe, Åsa, Zhang, Meng, Avraham-Davidi, Inbal, Vickovic, Sanja, Nitzan, Mor, Ma, Sai, Subramanian, Ayshwarya, Lipinski, Michal, Buenrostro, Jason, Brown, Nik Bear, Fanelli, Duccio, Zhuang, Xiaowei, Macosko, Evan Z, Regev, Aviv, Massachusetts Institute of Technology. Department of Biology, Biancalani, Tommaso, Scalia, Gabriele, Buffoni, Lorenzo, Avasthi, Raghav, Lu, Ziqing, Sanger, Aman, Tokcan, Neriman, Vanderburg, Charles R, Segerstolpe, Åsa, Zhang, Meng, Avraham-Davidi, Inbal, Vickovic, Sanja, Nitzan, Mor, Ma, Sai, Subramanian, Ayshwarya, Lipinski, Michal, Buenrostro, Jason, Brown, Nik Bear, Fanelli, Duccio, Zhuang, Xiaowei, Macosko, Evan Z, and Regev, Aviv

Abstract

AbstractCharting an organs’ biological atlas requires us to spatially resolve the entire single-cell transcriptome, and to relate such cellular features to the anatomical scale. Single-cell and single-nucleus RNA-seq (sc/snRNA-seq) can profile cells comprehensively, but lose spatial information. Spatial transcriptomics allows for spatial measurements, but at lower resolution and with limited sensitivity. Targeted in situ technologies solve both issues, but are limited in gene throughput. To overcome these limitations we present Tangram, a method that aligns sc/snRNA-seq data to various forms of spatial data collected from the same region, including MERFISH, STARmap, smFISH, Spatial Transcriptomics (Visium) and histological images. Tangram can map any type of sc/snRNA-seq data, including multimodal data such as those from SHARE-seq, which we used to reveal spatial patterns of chromatin accessibility. We demonstrate Tangram on healthy mouse brain tissue, by reconstructing a genome-wide anatomically integrated spatial map at single-cell resolution of the visual and somatomotor areas.

Published
2023

10. Integrative single cell and spatial transcriptomics of colorectal cancer reveals multicellular functional units that support tumor progression

Author

Avraham-Davidi, Inbal, primary, Mages, Simon, additional, Klughammer, Johanna, additional, Moriel, Noa, additional, Imada, Shinya, additional, Hofree, Matan, additional, Murray, Evan, additional, Chen, Jonathan, additional, Pelka, Karin, additional, Mehta, Arnav, additional, Boland, Genevieve M., additional, Delorey, Toni, additional, Caplan, Leah, additional, Dionne, Danielle, additional, Tirosh, Itay, additional, Hacohen, Nir, additional, Chen, Fei, additional, Yilmaz, Omer, additional, Roper, Jatin, additional, Rozenblatt-Rosen, Orit, additional, Nitzan, Mor, additional, and Regev, Aviv, additional

Published
2022
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12. ApoB-containing lipoproteins regulate angiogenesis by modulating expression of VEGF receptor 1

Author

Avraham-Davidi, Inbal, Ely, Yona, Pham, Van N., Castranova, Daniel, Grunspan, Moshe, Malkinson, Guy, Gibbs-Bar, Liron, Mayseless, Oded, Allmog, Gabriella, Lo, Brigid, Warren, Carmen M., Chen, Tom T., Ungos, Josette, Kidd, Kameha, Shaw, Kenna, Rogachev, Ilana, Wan, Wuzhou, Murphy, Philip M., Farber, Steven A., Carmel, Liran, Shelness, Gregory S., Iruela-Arispe, M. Luisa, Weinstein, Brant M., and Yaniv, Karina

Subjects
Blood lipoproteins -- Research -- Physiological aspects, Neovascularization -- Research -- Analysis, Proteolipids -- Research -- Physiological aspects, Lipoproteins -- Research -- Physiological aspects, Biological sciences, Health
Abstract

Despite the clear major contribution of hyperlipidemia to the prevalence of cardiovascular disease in the developed world, the direct effects of lipoproteins on endothelial cells have remained obscure and are under debate. Here we report a previously uncharacterized mechanism of vessel growth modulation by lipoprotein availability. Using a genetic screen for vascular defects in zebrafish, we initially identified a mutation, stalactite (stl), in the gene encoding microsomal triglyceride transfer protein (mtp), which is involved in the biosynthesis of apolipoprotein B (ApoB)-containing lipoproteins. By manipulating lipoprotein concentrations in zebrafish, we found that ApoB negatively regulates angiogenesis and that it is the ApoB protein particle, rather than lipid moieties within ApoB-containing lipoproteins, that is primarily responsible for this effect. Mechanistically, we identified downregulation of vascular endothelial growth factor receptor 1 (VEGFR1), which acts as a decoy receptor for VEGF, as a key mediator of the endothelial response to lipoproteins, and we observed VEGFR1 downregulation in hyperlipidemic mice. These findings may open new avenues for the treatment of lipoprotein-related vascular disorders., We identified a zebrafish mutant, which we named stalactite (stl), with excessive sprouting angiogenesis, in a Tg[(fli1:eGFP).sup.y1] (refs. 1, 2) transgenic-based forward genetic screen for vascular-specific mutations. stl mutants have [...]

Published
2012
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15. Parental-fetal interplay of immune genes leads to intrauterine growth restriction

Author

Kaur, Gurman, primary, Porter, Caroline B. M., additional, Ashenberg, Orr, additional, Lee, Jack, additional, Riesenfeld, Samantha J., additional, Hofree, Matan, additional, Aggelakopoulou, Maria, additional, Subramanian, Ayshwarya, additional, Kuttikkatte, Subita Balaram, additional, Attfield, Kathrine E., additional, Desel, Christiane A. E., additional, Davies, Jessica L., additional, Evans, Hayley G., additional, Avraham-Davidi, Inbal, additional, Nguyen, Lan T., additional, Dionne, Danielle A., additional, Neumann, Anna E., additional, Jensen, Lise Torp, additional, Barber, Thomas R., additional, Soilleux, Elizabeth, additional, Carrington, Mary, additional, McVean, Gil, additional, Rozenblatt-Rosen, Orit, additional, Regev, Aviv, additional, and Fugger, Lars, additional

Published
2021
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16. Deep learning and alignment of spatially-resolved whole transcriptomes of single cells in the mouse brain with Tangram

Author

Biancalani, Tommaso, primary, Scalia, Gabriele, additional, Buffoni, Lorenzo, additional, Avasthi, Raghav, additional, Lu, Ziqing, additional, Sanger, Aman, additional, Tokcan, Neriman, additional, Vanderburg, Charles R., additional, Segerstolpe, Asa, additional, Zhang, Meng, additional, Avraham-Davidi, Inbal, additional, Vickovic, Sanja, additional, Nitzan, Mor, additional, Ma, Sai, additional, Buenrostro, Jason, additional, Brown, Nik Bear, additional, Fanelli, Duccio, additional, Zhuang, Xiaowei, additional, Macosko, Evan Z., additional, and Regev, Aviv, additional

Published
2020
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18. Massively parallel single-nucleus RNA-seq with DroNc-seq

Author

Massachusetts Institute of Technology. Department of Biology, Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research at MIT, Koch Institute for Integrative Cancer Research at MIT, Zhang, Feng, Habib, Naomi, Choudhury, Sourav, Regev, Aviv, Avraham-Davidi, Inbal, Burks, Tyler, Shekhar, Karthik, Hofree, Matan, Aguet, François, Gelfand, Ellen, Ardlie, Kristin, Weitz, David A, Rozenblatt-Rosen, Orit, Basu, Anindita, 1978, Massachusetts Institute of Technology. Department of Biology, Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research at MIT, Koch Institute for Integrative Cancer Research at MIT, Zhang, Feng, Habib, Naomi, Choudhury, Sourav, Regev, Aviv, Avraham-Davidi, Inbal, Burks, Tyler, Shekhar, Karthik, Hofree, Matan, Aguet, François, Gelfand, Ellen, Ardlie, Kristin, Weitz, David A, Rozenblatt-Rosen, Orit, and Basu, Anindita, 1978

Abstract

Single-nucleus RNA sequencing (sNuc-seq) profiles RNA from tissues that are preserved or cannot be dissociated, but it does not provide high throughput. Here, we develop DroNc-seq: massively parallel sNuc-seq with droplet technology. We profile 39,111 nuclei from mouse and human archived brain samples to demonstrate sensitive, efficient, and unbiased classification of cell types, paving the way for systematic charting of cell atlases. Keywords: Cellular neuroscience; Gene expression; Gene expression analysis; RNA sequencing

Published
2018

19. DroNc-seq step-by-step v1

Author

Basu, Anindita, primary, Avraham-Davidi, Inbal, additional, Habib, Naomi, additional, Regev, Aviv, additional, Zhang, Feng, additional, Shekhar, Karthik, additional, Hofree, Matan, additional, Weitz, David, additional, Rozenblatt-Rosen, Orit, additional, Burks, Tyler, additional, Choudhury, Sourav, additional, Aguet, François, additional, Gelfand, Ellen, additional, and Ardlie, Kristin, additional

Published
2017
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20. DroNc-Seq: Deciphering cell types in human archived brain tissues by massively-parallel single nucleus RNA-seq

Author

Habib, Naomi, primary, Basu, Anindita, additional, Avraham-Davidi, Inbal, additional, Burks, Tyler, additional, Choudhury, Sourav R., additional, Aguet, François, additional, Gelfand, Ellen, additional, Ardlie, Kristin, additional, Weitz, David A, additional, Rozenblatt-Rosen, Orit, additional, Zhang, Feng, additional, and Regev, Aviv, additional

Published
2017
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21. Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons

Author

Massachusetts Institute of Technology. Department of Biological Engineering, Massachusetts Institute of Technology. Department of Biology, Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, McGovern Institute for Brain Research at MIT, Koch Institute for Integrative Cancer Research at MIT, Habib, Naomi, Li, Yinqing, Heidenreich, Matthias, Swiech, Lukasz, Zhang, Feng, Regev, Aviv, Avraham-Davidi, Inbal, Trombetta, John J., Hession, Cynthia, Massachusetts Institute of Technology. Department of Biological Engineering, Massachusetts Institute of Technology. Department of Biology, Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, McGovern Institute for Brain Research at MIT, Koch Institute for Integrative Cancer Research at MIT, Habib, Naomi, Li, Yinqing, Heidenreich, Matthias, Swiech, Lukasz, Zhang, Feng, Regev, Aviv, Avraham-Davidi, Inbal, Trombetta, John J., and Hession, Cynthia

Abstract

Single-cell RNA sequencing (RNA-Seq) provides rich information about cell types and states. However, it is difficult to capture rare dynamic processes, such as adult neurogenesis, because isolation of rare neurons from adult tissue is challenging and markers for each phase are limited. Here, we develop Div-Seq, which combines scalable single-nucleus RNA-Seq (sNuc-Seq) with pulse labeling of proliferating cells by 5-ethynyl-2′-deoxyuridine (EdU) to profile individual dividing cells. sNuc-Seq and Div-Seq can sensitively identify closely related hippocampal cell types and track transcriptional dynamics of newborn neurons within the adult hippocampal neurogenic niche, respectively. We also apply Div-Seq to identify and profile rare newborn neurons in the adult spinal cord, a noncanonical neurogenic region. sNuc-Seq and Div-Seq open the way for unbiased analysis of diverse complex tissues., National Institute of Mental Health (U.S.) (Grant U01MH105960), National Institute of Mental Health (U.S.) (Grant 5DP1-MH100706), National Institute of Mental Health (U.S.) (Grant 1R01-MH110049)

Published
2017

22. Zebrafish as a model for apolipoprotein biology: comprehensive expression analysis and a role for ApoA-IV in regulating food intake

Author

Otis, Jessica P., primary, Zeituni, Erin M., additional, Thierer, James H., additional, Anderson, Jennifer L., additional, Brown, Alexandria C., additional, Boehm, Erica D., additional, Cerchione, Derek M., additional, Ceasrine, Alexis M., additional, Avraham-Davidi, Inbal, additional, Tempelhof, Hanoch, additional, Yaniv, Karina, additional, and Farber, Steven A., additional

Published
2015
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23. On-site education of VEGF-recruited monocytes improves their performance as angiogenic and arteriogenic accessory cells

Author

Avraham-Davidi, Inbal, primary, Yona, Simon, additional, Grunewald, Myriam, additional, Landsman, Limor, additional, Cochain, Clement, additional, Silvestre, Jean Sebastien, additional, Mizrahi, Haim, additional, Faroja, Mohammad, additional, Strauss-Ayali, Dalit, additional, Mack, Matthias, additional, Jung, Steffen, additional, and Keshet, Eli, additional

Published
2013
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