CAJAL enables analysis and integration of single-cell morphological data using metric geometry.
CAJAL enables analysis and integration of single-cell morphological data using metric geometry.
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DOI：
10.1038/s41467-023-39424-2
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发表时间：
2023-06-21
影响因子：
16.6
通讯作者：
Camara, Pablo G.
中科院分区：
文献类型：
--
作者：
Govek, Kiya W.;Nicodemus, Patrick;Lin, Yuxuan;Crawford, Jake;Saturnino, Artur B.;Cui, Hannah;Zoga, Kristi;Hart, Michael P.;Camara, Pablo G.
高分辨率成像彻底改变了单细胞空间背景下的研究。然而，总结组织中复杂细胞形状的巨大多样性并推断与其他单细胞数据的关联仍然是一个挑战。在这里，我们提出了 CAJAL，一种用于分析和整合单细胞形态数据的通用计算框架。通过建立在度量几何的基础上，CAJAL 推断出细胞形态潜在空间，其中点之间的距离表示将一个细胞的形态改变为另一个细胞的形态所需的物理变形量。我们表明，细胞形态空间有助于跨技术的单细胞形态数据的整合以及与其他数据（例如单细胞转录组数据）关系的推断。我们通过几个神经元和神经胶质细胞的形态数据集展示了 CAJAL 的实用性，并鉴定了与线虫神经元可塑性相关的基因。我们的方法提供了将细胞形态数据整合到单细胞组学分析中的有效策略。细胞形态是生物学中描述最多的表型之一，但形态的系统量化和分类仍然有限。在这里，作者提出了一种基于度量几何概念的细胞形态测量和多模态分析的计算方法。
High-resolution imaging has revolutionized the study of single cells in their spatial context. However, summarizing the great diversity of complex cell shapes found in tissues and inferring associations with other single-cell data remains a challenge. Here, we present CAJAL, a general computational framework for the analysis and integration of single-cell morphological data. By building upon metric geometry, CAJAL infers cell morphology latent spaces where distances between points indicate the amount of physical deformation required to change the morphology of one cell into that of another. We show that cell morphology spaces facilitate the integration of single-cell morphological data across technologies and the inference of relations with other data, such as single-cell transcriptomic data. We demonstrate the utility of CAJAL with several morphological datasets of neurons and glia and identify genes associated with neuronal plasticity in C. elegans. Our approach provides an effective strategy for integrating cell morphology data into single-cell omics analyses. Cell morphology is one of the most described phenotypes in biology, yet systematic quantification and classification of morphology remains limited. Here, the authors present a computational approach for cell morphometry and multi-modal analysis based on concepts from metric geometry.
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影响因子：
46.9
作者：
Fuzik J;Zeisel A;Máté Z;Calvigioni D;Yanagawa Y;Szabó G;Linnarsson S;Harkany T
通讯作者：
Harkany T
影响因子：
46.9
作者：
Becht, Etienne;McInnes, Leland;Newell, Evan W.
通讯作者：
Newell, Evan W.
影响因子：
25
作者：
Gouwens NW;Sorensen SA;Berg J;Lee C;Jarsky T;Ting J;Sunkin SM;Feng D;Anastassiou CA;Barkan E;Bickley K;Blesie N;Braun T;Brouner K;Budzillo A;Caldejon S;Casper T;Castelli D;Chong P;Crichton K;Cuhaciyan C;Daigle TL;Dalley R;Dee N;Desta T;Ding SL;Dingman S;Doperalski A;Dotson N;Egdorf T;Fisher M;de Frates RA;Garren E;Garwood M;Gary A;Gaudreault N;Godfrey K;Gorham M;Gu H;Habel C;Hadley K;Harrington J;Harris JA;Henry A;Hill D;Josephsen S;Kebede S;Kim L;Kroll M;Lee B;Lemon T;Link KE;Liu X;Long B;Mann R;McGraw M;Mihalas S;Mukora A;Murphy GJ;Ng L;Ngo K;Nguyen TN;Nicovich PR;Oldre A;Park D;Parry S;Perkins J;Potekhina L;Reid D;Robertson M;Sandman D;Schroedter M;Slaughterbeck C;Soler-Llavina G;Sulc J;Szafer A;Tasic B;Taskin N;Teeter C;Thatra N;Tung H;Wakeman W;Williams G;Young R;Zhou Z;Farrell C;Peng H;Hawrylycz MJ;Lein E;Ng L;Arkhipov A;Bernard A;Phillips JW;Zeng H;Koch C
通讯作者：
Koch C
影响因子：
12.3
作者：
Carpenter AE;Jones TR;Lamprecht MR;Clarke C;Kang IH;Friman O;Guertin DA;Chang JH;Lindquist RA;Moffat J;Golland P;Sabatini DM
通讯作者：
Sabatini DM
影响因子：
11
作者：
Bardy, C.;van den Hurk, M.;Kakaradov, B.;Erwin, J. A.;Jaeger, B. N.;Hernandez, R. V.;Eames, T.;Paucar, A. A.;Gorris, M.;Marchand, C.;Jappelli, R.;Barron, J.;Bryant, A. K.;Kellogg, M.;Lasken, R. S.;Rutten, B. P. F.;Steinbusch, H. W. M.;Yeo, G. W.;Gage, F. H.
通讯作者：
Gage, F. H.