PySpacell: A Python Package for Spatial Analysis of Cell Images

Technologies such as microscopy, sequential hybridization, and mass spectrometry enable quantitative single‐cell phenotypic and molecular measurements in situ. Deciphering spatial phenotypic and molecular effects on the single‐cell level is one of the grand challenges and a key to understanding the effects of cell–cell interactions and microenvironment. However, spatial information is usually overlooked by downstream data analyses, which usually consider single‐cell read‐out values as independent measurements for further averaging or clustering, thus disregarding spatial locations. With this work, we attempt to fill this gap. We developed a toolbox that allows one to test for the presence of a spatial effect in microscopy images of adherent cells and estimate the spatial scale of this effect. The proposed Python module can be used for any light microscopy images of cells as well as other types of single‐cell data such as in situ transcriptomics or metabolomics. The input format of our package matches standard output formats from image analysis tools such as CellProfiler, Fiji, or Icy and thus makes our toolbox easy and straightforward to use, yet offering a powerful statistical approach for a wide range of applications. © 2019 International Society for Advancement of Cytometry

[1]  M. Newman,et al.  Mixing patterns in networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  P. Liberali,et al.  Population context determines cell-to-cell variability in endocytosis and virus infection , 2009, Nature.

[3]  Janusz Szmyt,et al.  Spatial statistics in ecological analysis: from indices to functions , 2014 .

[4]  Long Cai,et al.  Single cell systems biology by super-resolution imaging and combinatorial labeling , 2012, Nature Methods.

[5]  S. Teichmann,et al.  SpatialDE: identification of spatially variable genes , 2018, Nature Methods.

[6]  Theodore Alexandrov,et al.  Spatial single-cell profiling of intracellular metabolomes in situ , 2019, bioRxiv.

[7]  Shantanu Singh,et al.  Capturing single-cell heterogeneity via data fusion improves image-based profiling , 2018 .

[8]  Jin‐Ming Lin,et al.  Single-Cell Analysis Using Drop-on-Demand Inkjet Printing and Probe Electrospray Ionization Mass Spectrometry. , 2016, Analytical chemistry.

[9]  William E. Allen,et al.  Three-dimensional intact-tissue sequencing of single-cell transcriptional states , 2018, Science.

[10]  Peter Horvath,et al.  Environmental properties of cells improve machine learning-based phenotype recognition accuracy , 2018, Scientific Reports.

[11]  Rickard Sandberg,et al.  Identification of spatial expression trends in single-cell gene expression data , 2018, Nature Methods.

[12]  D. Pe’er,et al.  Trajectories of cell-cycle progression from fixed cell populations , 2015, Nature Methods.

[13]  Ivo F. Sbalzarini,et al.  MosaicIA: an ImageJ/Fiji plugin for spatial pattern and interaction analysis , 2013, BMC Bioinformatics.

[14]  Patrik L. Ståhl,et al.  Visualization and analysis of gene expression in tissue sections by spatial transcriptomics , 2016, Science.

[15]  Johannes E. Schindelin,et al.  Fiji: an open-source platform for biological-image analysis , 2012, Nature Methods.

[16]  A. T. Doyle,et al.  Structure Functions , 1998 .

[17]  Lior Pachter,et al.  Single-cell analysis at the threshold , 2016, Nature Biotechnology.

[18]  Thibaut Jombart,et al.  Revisiting Guerry’s data: Introducing spatial constraints in multivariate analysis , 2011, 1202.6485.

[19]  Thomas Boudier,et al.  DiAna, an ImageJ tool for object-based 3D co-localization and distance analysis. , 2017, Methods.

[20]  Guocheng Yuan,et al.  Identification of spatially associated subpopulations by combining scRNA-seq and sequential fluorescence in situ hybridization data , 2018, Nature Biotechnology.

[21]  P. Dixon.  Ripley's K Function , 2006 .

[22]  Sergio J. Rey,et al.  PySAL: A Python Library of Spatial Analytical Methods , 2010 .

[23]  Thibault Lagache,et al.  Statistical analysis of molecule colocalization in bioimaging , 2015, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[24]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[25]  G. Superti-Furga,et al.  Global survey of the immunomodulatory potential of common drugs , 2017, Nature chemical biology.

[26]  Sean C. Bendall,et al.  A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging , 2018, Cell.

[27]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..