ImmGen at 15

Aguilar, Stephanie Vargas; Aguilar, Oscar A.; Allan, Rhys S.; Amir, El ad David; Angeli, Véronique; Artyomov, Maxim N.; Asinovski, Natasha; Astarita, Jilian; Austen, K. Frank; Bajpai, Geetika; Barrett, Nora A.; Baysoy, Alev; Benoist, Christophe; Bellemare-Pelletier, Angélique; Berg, Brad; Best, Adam J; Bezman, Natalie; Blair, David A.; Blander, J. Magarian; Bogunovic, Milena; Brennan, Patrick McKinley; Brenner, Michael; Brown, Brian D.; Buechler, Matthew B.; Buenrostro, Jason D.; Casanova, Maria Acebes; Choi, Kyunghee; Chow, Andrew; Chudnovskiy, Aleksey; Cipoletta, Daniela; Cohen, Nadia; Collins, James J.; Colonna, Marco; Cook, Alison; Costello, James C.; Cremasco, Viviana; Crowl, Ty; Crozat, Karine; Cruse, Richard; D’Angelo, June A.; Dalod, Marc; Davis, Scott; Demiralp, Çağatay; Deng, Tianda; Desai, Jigar V.; Desland, Fiona; Dhainaut, Maxime; Ding, Jiarui; Doedens, Andrew L.; Dominguez, Claudia X.; Doran, Graeme; Dress, Regine J.; Dustin, Michael L.; Dwyer, Daniel F.; Dzhagalov, Ivan; Elpek, Kutlu G.; Ergün, Ayla; Ericson, Jeff; Esomonu, Eunice; Fairfax, Keke C.; Fletcher, Anne; Frascoli, Michela; Fuchs, Anja; Gainullina, Anastasiia; Gal-Oz, Shani T.; Gallagher, Michael; Gautier, Emmanuel L.; Gazit, Roi; Gibbings, Sophie L.; Giraud, Matthieu; Ginhoux, Florent; Goldrath, Ananda W.; Gotthardt, Dagmar; Gray, Daniel H.D.; Greter, Melanie; Grieshaber-Bouyer, Ricardo; Guilliams, Martin; Haidermota, Sara; Hardy, Randy; Hashimoto, Daigo; Helft, Julie; Hendricks, Deborah W.; Heng, Tracy; Hill, Jonathan A.; Hyatt, Gordon; Idoyaga, Juliana; Jakubzick, Claudia; Jarjoura, Jessica; Jepson, Daniel A.; Jia, Baosen; Jianu, Radu; Johanson, Tim; Jojic, Vladimir; Jordan, Stefan; Kamimura, Yoshitsugu; Kana, Veronika; Kang, Joonsoo; Kapoor, Varun; Kenigsberg, Ephriam; Kent, Andrew; Kim, Charles; Kim, Edy Y.; Kim, Francis; Kim, Joel; Kim, Ki Wook; Kiner, Evgeny; Knell, Jamie; Koller, Daphne; Kozinn, Larry; Krchma, Karen; Kreslavsky, Taras; Kronenberg, Mitchell; Kwan, Wing-Hong; Laidlaw, David H.; Lam, Viola; Lanier, Lewis L.; Laplace, Catherine; Lareau, Caleb A.; Lavin, Yonit; Lavine, Kory J.; Leader, Andrew M.; Leboeuf, Marylène; Jacob, Louis; Lee, Jisu; Li, Bo; Hu, Li; Li, Yuesheng; Lionakis, Michail S.; Luche, Hervé; Lynch, Lydia; Magen, Assaf; Maier, Bárbara; Malhotra, Deepali; Malhotra, Nidhi; Malissen, Marie; Maslova, Alexandra; Mathis, Diane; McFarland, Adelle P.; Mérad, Miriam; Meunier, Étienne; Miller, Jennifer C.; Milner, J. Justin; Mingueneau, Michaël; Min‐Oo, Gundula; Monach, Paul A.; Moodley, Devapregasan; Mortha, Arthur; Morvan, Maelig; Mostafavi, Sara; Müller, Sören; Muus, Christoph; Nabekura, Tsukasa; Rao, Tata Nageswara; Narang, Vipin; Narayan, Kavitha; Ner-Gaon, Hadas; Nguyen, Quyhn; Nigrović, Peter A.; Novakovsky, Gherman; Nutt, Stephen L.; Omilusik, Kyla; Ortiz-Lopez, Adriana; Païdassi, Helena; Paik, Henry; Painter, Michio W.; Paynich, Mallory; Peng, Vincent; Potempa, Marc; Pradhan, Rachana; Price, Jeremy; Qi, Yilin; Qi, Yiqing; Quon, Sara; Ramirez, Ricardo N.; Ramanan, Deepshika; Randolph, Gwendalyn J.; Regev, Aviv; Rhoads, Andrew; Robinette, Michelle L.; Rose, Samuel A.; Rossi, Derrick J.; Rothamel, Katherine; Sachidanandam, Ravi; Sathe, Priyanka; Scott, Charlotte; Seddu, Kumba; See, Peter; Sergushichev, Alexey; Shaw, Laura; Shay, Tal; Shemesh, Avishai; Shinton, Susan A.; Shyer, Justin A.; Sieweke, Michael H.; Smillie, Christopher; Spel, Lotte; Spidale, Nick; Stifano, Giuseppina; Subramanian, Ayshwarya; Sun, Joseph C.; Sylvia, Katelyn; Tellier, Julie; This, Sébastien; Tomasello, Elena; Todorov, Helena; Turley, Shannon J.; Vijaykumar, Brinda; Wagers, Amy J.; Wakamatsu, Ei; Wang, Chendi; Wang, Peter L.; Wroblewska, Aleksandra; Wu, Jun; Yang, Edward; Yang, Liang; Yim, Aldrin Kay-Yuen; Yng, Lim Sheau; Yoshida, Hideyuki; Yu, Bingfei; Zhou, Yan; Zhu, Yanan; Ziemkiewicz, Caroline

doi:10.1038/s41590-020-0687-4

Cited by 60 publications

(23 citation statements)

References 22 publications

(45 reference statements)

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“…To identify dominant cell types of each sample library, the scRNA-seq-based cell type recognition tool SingleR (version 1.0.6) was repurposed and applied to the bulk RNA-seq dataset at hand (Aran et al , 2019). Default parameters were used to compute the correlation of each sample against the curated ImmGen reference dataset (Jojic et al , 2013; Shay and Kang, 2013; Aguilar et al , 2020). In particular, subtypes within the broad hematopoietic stem cell compartment were used as reference.…”

Section: Methodsmentioning

confidence: 99%

A highly efficient reporter system for identifying and characterizing in vitro expanded hematopoietic stem cells

Jlc

Bode

Kuciński

et al. 2021

Preprint

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Hematopoietic stem cells (HSCs) cultured outside the body are the fundamental component of a wide range of cellular and gene therapies. Recent efforts have achieved more than 200-fold expansion of functional HSCs, but their molecular characterization has not been possible due to the substantial majority of cells being non-HSCs and single cell-initiated cultures displaying substantial clone-to-clone variability. Using the Fgd5 reporter mouse in combination with the EPCR surface marker, we report exclusive identification of HSCs from non-HSCs in expansion cultures. Linking single clone functional transplantation data with single clone gene expression profiling, we show that the molecular profile of expanded HSCs is similar to actively cycling fetal liver HSCs and shares a gene expression signature with functional HSCs from all sources, including Prdm16, Fstl1 and Palld. This new tool can now be applied to a wide-range of functional screening and molecular experiments previously not possible due to limited HSC numbers.

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Section: Methodsmentioning

confidence: 99%

A highly efficient reporter system for identifying and characterizing in vitro expanded hematopoietic stem cells

Jlc

Bode

Kuciński

et al. 2021

Preprint

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“…This is accompanied with an increased interaction of endothelial cells with WBCs, as cell adhesion molecules on the vessel lining direct WBC trafficking to infected RBC sequestration sites ( 53 ). In addition, immune cells like B cells and monocytes express adhesion-related genes found in this GO term, like Fer (tyrosine-protein kinase) and ICAM-1 (intercellular adhesion molecule 1), respectively ( 54 – 56 ). To connect cooccurring host and parasite biological processes, we found associations between enriched ( P value ≤ 0.05) host and parasite GO terms based on the interactors of the genes in these enriched GO terms.…”

Section: Resultsmentioning

confidence: 99%

Dual RNA Sequencing Meta-analysis in Plasmodium Infection Identifies Host-Parasite Interactions

2021

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Dual RNA sequencing (RNA-Seq) is the simultaneous transcriptomic analysis of interacting symbionts, for example, in malaria. Potential cross-species interactions identified by correlated gene expression might highlight interlinked signaling, metabolic, or gene regulatory pathways in addition to physically interacting proteins. Often, malaria studies address one of the interacting organisms—host or parasite—rendering the other “contamination.” Here we perform a meta-analysis using such studies for cross-species expression analysis. We screened experiments for gene expression from host and Plasmodium. Out of 171 studies in Homo sapiens, Macaca mulatta, and Mus musculus, we identified 63 potential studies containing host and parasite data. While 16 studies (1,950 samples) explicitly performed dual RNA-Seq, 47 (1,398 samples) originally focused on one organism. We found 915 experimental replicates from 20 blood studies to be suitable for coexpression analysis and used orthologs for meta-analysis across different host-parasite systems. Centrality metrics from the derived gene expression networks correlated with gene essentiality in the parasites. We found indications of host immune response to elements of the Plasmodium protein degradation system, an antimalarial drug target. We identified well-studied immune responses in the host with our coexpression networks, as our approach recovers known broad processes interlinked between hosts and parasites in addition to individual host and parasite protein associations. The set of core interactions represents commonalities between human malaria and its model systems for prioritization in laboratory experiments. Our approach might also allow insights into the transferability of model systems for different pathways in malaria studies. IMPORTANCE Malaria still causes about 400,000 deaths a year and is one of the most studied infectious diseases. The disease is studied in mice and monkeys as lab models to derive potential therapeutic intervention in human malaria. Interactions between Plasmodium spp. and its hosts are either conserved across different host-parasite systems or idiosyncratic to those systems. Here we use correlation of gene expression from different RNA-Seq studies to infer common host-parasite interactions across human, mouse, and monkey studies. First, we find a set of very conserved interactors, worth further scrutiny in focused laboratory experiments. Second, this work might help assess to which extent experiments and knowledge on different pathways can be transferred from models to humans for potential therapy.

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“…We further evaluated the performance of the CellKb Immune by checking the number of correct cell types assigned to gene lists obtained from a bulk RNA-seq dataset of mouse immune cell types (12) from Immgen (17). Using the top1,500 significantly upregulated genes for each cell type in the bulk RNA-seq dataset, we searched the CellKb Immune database for matching marker gene sets.…”

Section: Discussionmentioning

confidence: 99%

“…We further evaluated the performance of the CellKb Immune using marker gene lists from Immgen 15 (which is not included in CellKb Immune). Using the most significantly upregulated genes for each cell type in Immgen, we searched the CellKb Immune database for matching cell types.…”

Section: ) Browse Cell Typesmentioning

confidence: 99%

CellKb Immune: a manually curated database of mammalian hematopoietic marker gene sets for rapid cell type identification

Patil¹,

Patil²

2020

Preprint

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Single-cell RNA-seq is widely used to study transcriptional patterns of genes in individual cells. In spite of current advances in technology, assigning cell types in single-cell datasets remains a bottleneck due to the lack of a comprehensive reference database and a fast search method in a single tool. CellKb Immune is a knowledgebase of manually collected, curated and annotated marker gene sets from cell types in the mammalian immune response. It finds matching cell types in literature given a list of genes using a novel rank-based algorithm optimized for rapid searching across marker gene lists of differing lengths. We evaluated the contents and search algorithm of CellKb Immune using a leave-one-out approach. We further used CellKb Immune to annotate previously defined marker gene sets from Immgen to confirm its accuracy and coverage. CellKb Immune provides an easy to use database with a fast and reliable method to find matching cell types and annotate cells in single-cell experiments in a single tool. It is available at https://www.cellkb.com/immune.

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ImmGen at 15

Cited by 60 publications

References 22 publications

A highly efficient reporter system for identifying and characterizing in vitro expanded hematopoietic stem cells

A highly efficient reporter system for identifying and characterizing in vitro expanded hematopoietic stem cells

Dual RNA Sequencing Meta-analysis in Plasmodium Infection Identifies Host-Parasite Interactions

CellKb Immune: a manually curated database of mammalian hematopoietic marker gene sets for rapid cell type identification

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