1
|
Biancalani T, Scalia G, Buffoni L, Avasthi R, Lu Z, Sanger A, Tokcan N, Vanderburg CR, Segerstolpe Å, Zhang M, Avraham-Davidi I, Vickovic S, Nitzan M, Ma S, Subramanian A, Lipinski M, Buenrostro J, Brown NB, Fanelli D, Zhuang X, Macosko EZ, Regev A. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat Methods 2021; 18:1352-1362. [PMID: 34711971 PMCID: PMC8566243 DOI: 10.1038/s41592-021-01264-7] [Citation(s) in RCA: 205] [Impact Index Per Article: 68.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 08/11/2021] [Indexed: 11/08/2022]
Abstract
Charting 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.
Collapse
Affiliation(s)
- Tommaso Biancalani
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Genentech, South San Francisco, CA, USA.
| | - Gabriele Scalia
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Roche, Monza, Italy
| | - Lorenzo Buffoni
- Department of Physics and Astrophysics, University of Florence, Florence, Italy
| | - Raghav Avasthi
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Northeastern University, Boston, MA, USA
| | - Ziqing Lu
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Northeastern University, Boston, MA, USA
| | - Aman Sanger
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | | | - Meng Zhang
- Department of Chemistry and Chemical Biology, Department of Physics, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | | | | | - Mor Nitzan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- School of Computer Science and Engineering, Racah Institute of Physics, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Sai Ma
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, MIT, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | | | - Michal Lipinski
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Jason Buenrostro
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | | | - Duccio Fanelli
- Department of Physics and Astrophysics, University of Florence, Florence, Italy
| | - Xiaowei Zhuang
- Department of Chemistry and Chemical Biology, Department of Physics, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | | | - Aviv Regev
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biology, MIT, Cambridge, MA, USA.
- Genentech, South San Francisco, CA, USA.
- Howard Hughes Medical Institute, Chevy Chase, MD, USA.
| |
Collapse
|
2
|
Hernandez L, Kim R, Tokcan N, Derksen H, Biesterveld BE, Croteau A, Williams AM, Mathis M, Najarian K, Gryak J. Multimodal tensor-based method for integrative and continuous patient monitoring during postoperative cardiac care. Artif Intell Med 2021; 113:102032. [PMID: 33685593 DOI: 10.1016/j.artmed.2021.102032] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 01/06/2021] [Accepted: 02/08/2021] [Indexed: 11/26/2022]
Abstract
Patients recovering from cardiovascular surgeries may develop life-threatening complications such as hemodynamic decompensation, making the monitoring of patients for such complications an essential component of postoperative care. However, this need has given rise to an inexorable increase in the number and modalities of data points collected, making it challenging to effectively analyze in real time. While many algorithms exist to assist in monitoring these patients, they often lack accuracy and specificity, leading to alarm fatigue among healthcare practitioners. In this study we propose a multimodal approach that incorporates salient physiological signals and EHR data to predict the onset of hemodynamic decompensation. A retrospective dataset of patients recovering from cardiac surgery was created and used to train predictive models. Advanced signal processing techniques were employed to extract complex features from physiological waveforms, while a novel tensor-based dimensionality reduction method was used to reduce the size of the feature space. These methods were evaluated for predicting the onset of decompensation at varying time intervals, ranging from a half-hour to 12 h prior to a decompensation event. The best performing models achieved AUCs of 0.87 and 0.80 for the half-hour and 12-h intervals respectively. These analyses evince that a multimodal approach can be used to develop clinical decision support systems that predict adverse events several hours in advance.
Collapse
Affiliation(s)
- Larry Hernandez
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States
| | - Renaid Kim
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States
| | - Neriman Tokcan
- The Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA 02142, United States
| | - Harm Derksen
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, United States
| | - Ben E Biesterveld
- Department of Surgery, University of Michigan, Ann Arbor, MI 48109, United States
| | - Alfred Croteau
- Hartford HealthCare Medical Group, Hartford, CT 06106, United States
| | - Aaron M Williams
- Department of Surgery, University of Michigan, Ann Arbor, MI 48109, United States
| | - Michael Mathis
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI 48109, United States
| | - Kayvan Najarian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States; Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, United States; Michigan Center for Integrative Research in Critical Care (MCIRCC), University of Michigan, Ann Arbor, MI 48109, United States; Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI 48109, United States
| | - Jonathan Gryak
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States; Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI 48109, United States.
| |
Collapse
|