Slide 1

Slide 1 text

Introduction to multiplex single-cell imaging data Julia Wrobel, PhD BDSI 2024

Slide 2

Slide 2 text

First, a bit about my path to biostatistics… 2

Slide 3

Slide 3 text

2010 Swarthmore College • BA, chemistry

Slide 4

Slide 4 text

2010 2013 Swarthmore College • BA, chemistry Children’s Hospital of Phila. • Immunology research asst

Slide 5

Slide 5 text

2010 2013 Swarthmore College • BA, chemistry Children’s Hospital of Phila. • Immunology research asst 2019 Columbia University • MS, PhD, biostatistics

Slide 6

Slide 6 text

2010 2013 Swarthmore College • BA, chemistry Children’s Hospital of Phila. • Immunology research asst 2019 Columbia University • MS, PhD, biostatistics 2024 Assistant Professor • Colorado/Emory • Biostatistics and Bioinformatics

Slide 7

Slide 7 text

Some takeaways • Your path doesn’t have to be linear • A great thing about academia is you get to keep learning • A great thing about biostatistics is dabbling in different scientific areas • For example, cancer biology 7

Slide 8

Slide 8 text

Back to single cell multiplex imaging 8

Slide 9

Slide 9 text

What is single cell multiplex imaging? • High dimensional analysis of tissue samples at the resolution of individual cells 9

Slide 10

Slide 10 text

What is single cell multiplex imaging? • Single cell refers to individual cell resolution 10

Slide 11

Slide 11 text

What is single cell multiplex tissue imaging? • Multiplex refers to multiple types of proteins in the tissue that are tagged • Each protein label is called a marker • Phenotypic markers: used to define cell and/or tissue type • Functional markers: inform cell function • Present across multiple cell types 11

Slide 12

Slide 12 text

What is single cell multiplex tissue imaging? • Multiplex refers to multiple types of proteins in the tissue that are tagged • Immunofluorescence based • Proteins stained with fluorescent antibodies then imaged using fluorescence microscopy • Mass cytometry based • Proteins tagged with metal isotypes (IMC, MIBI)

Slide 13

Slide 13 text

What is single cell multiplex imaging? • Imaging means spatial relationships in the tissue are preserved • Precursor technologies (left) required suspension of cells in solution, destroying spatial info 13

Slide 14

Slide 14 text

What is single cell multiplex tissue imaging? • Images produced are multichannel TIFF (.tif) files • Each channel is a different protein marker • Each pixel contains a continuous intensity value for each marker • Example below with non-small cell lung cancer data • 8 channels, 3 shown (Left to Right: composite image, nuclei, CK, CD8) 14

Slide 15

Slide 15 text

Cancer, multiplex imaging, and the tumor microenvironment The tumor microenvironment (TME) is the area within and surrounding a tumor, including tumor cells, infiltrating immune cells, blood vessels, and other tissue • What percentages of immune cell subtypes are present before and after chemotherapy? • Do patients with high spatial clustering of B-cells and Macrophages survive longer? 15 Image from Polidoro et. al. World J. Gasteroentrol., 26(33) (2020)

Slide 16

Slide 16 text

Multiplex image data processing and analysis pipeline Phenotyping Segmentation Normalization Image processing Compositional data analysis Spatial data analysis Statistical analysis Image acquisition Image filtering/ background correction Image pre-processing

Slide 17

Slide 17 text

Image acquisition Image filtering/ background correction Multiplex image data processing and analysis pipeline Image acquisition Image filtering/ background correction Image pre-processing Phenotyping Segmentation Normalization Image processing

Slide 18

Slide 18 text

Multiplex image data processing and analysis pipeline Cell 1 Pixel 1 Marker a Marker b Pixel n Marker a Marker b Phenotyping Segmentation Normalization Image processing

Slide 19

Slide 19 text

Code Interlude 1: TIFF data 19

Slide 20

Slide 20 text

Multiplex image data processing and analysis pipeline Phenotyping Segmentation Normalization Image processing Image # Cell # CD4 CD19 Phenotype X Y Tissue type 1 1 + _ CD4 T-cell … … Tumor 1 2 - + B-cell … … Stroma 1 3 - - Macrophage … … Stroma 1 4 + - CD8 T-cell … … Tumor 2 1 - - Tumor cell … … Stroma

Slide 21

Slide 21 text

Multiplex image data processing and analysis pipeline Compositional data analysis Spatial data analysis Statistical analysis Image # Cell # Phenotype X Y Tissue type Age Survival Status Cancer stage 1 1 CD4 T-cell … … Tumor 55 0 1 1 2 B-cell … … Stroma 55 0 1 1 3 Macrophage … … Stroma 55 0 1 1 4 CD8 T-cell … … Tumor 55 0 1 2 1 Tumor cell … … Stroma 62 1 2 Cell-level Image-level Patient-level

Slide 22

Slide 22 text

• Surprisingly little multiplex-imaging data is publicly available • VectraPolarisData package addresses this • Bioconductor ExperimentHub package as of April 2022 • 2 large datasets from my collaborators at CU-Anschutz VectraPolarisData Package 22

Slide 23

Slide 23 text

VectraPolarisData datasets HumanLungCancerV3 • Non-small cell lung carcinoma ROIs • Vectra3 mIHC, CU-AMC • 761 images from 153 patients • 1,604,786 • 7 markers • 5 phenotypic (CD3, CD8, CD14, CD19, CD68, ck) • 1 functional (HLADR) • Patient-level outcomes HumanOvarianCancerVP • High-grade serous ovarian cancer tumor microarray • VectraPolaris mIHC, CU-AMC • 128 patients, 1 image each • 1,610,431 cells • 8 markers • 5 phenotypic (CD3, CD8, CD68, CD19, CK) • 2 functional (pstat3, IER3) • Patient-level outcomes 23

Slide 24

Slide 24 text

Code Interlude 2: VectraPolarisData 24

Slide 25

Slide 25 text

Statistical image processing 25

Slide 26

Slide 26 text

• Pixel level processing: work with multichannel tiff files directly • Operates on pixel intensity values • Cell-level processing: work with tabular data after cell segmentation • Operates on median or mean intensity values aggregated at cell level • Segmentation • Pixel-level • Phenotyping • Pixel-level or cell-level Statistical image processing 26

Slide 27

Slide 27 text

Identifies cells and nuclei in image 1. Nucleus channel used to identify nucleus 2. Cell membrane or cytoplasm markers used to draw boundary around nucleus Cell segmentation 27 Cell 1 Pixel 1 Marker a Marker b Pixel n Marker a Marker b

Slide 28

Slide 28 text

• Proprietary software • inForm by Akoya, Halo automated image analysis software • GUI-based, user-friendly • No manually segmented data required • GUI-based open-source software • CellProfiler, ilastik, QuPath • User-friendly for non-computer scientists/statisticians • No manually segmented data required* • Deep-learning based open-source software • Best performance* • Need manually segmented data* • Hard to adapt without computational expertise Segmentation approaches 28

Slide 29

Slide 29 text

Cell phenotyping The process of identifying cell types from marker expression values • Cell labeling Conceptually, goal is to create a “cut point” in marker intensities where cells are either positive or negative for a marker 29

Slide 30

Slide 30 text

Cell types by immune markers Thanks to Brooke Fridley and Alex Soupir at the Moffitt Cancer Center for this image

Slide 31

Slide 31 text

• Marker gating • Determine cutpoint in marker intensity histogram to designate “marker positive” and “marker negative” cells • Unsupervised clustering methods • Seurat, Phenograph contain built-in software • Most developed for other single-cell analysis • Semi-supervised • GammaGateR: new approach by Xiong, Vandekar, Bioinformatics 2024 Phenotyping approaches 31

Slide 32

Slide 32 text

Spatial analysis 32

Slide 33

Slide 33 text

Our goal is to use multiplex imaging to better understand patient survival in ovarian cancer • Cell locations from an ovarian cancer tissue sample • Green cells are in tumor area • Pink cells are in stromal area • Black cells are macrophages • Steps of analysis: 1. Quantify spatial clustering of macrophages 2. Use spatial metrics as covariates in regression model How to best quantify spatial clustering? • Need to do feature extraction 33

Slide 34

Slide 34 text

• Why can’t we compare the images themselves directly? • Highly heterogeneous structure • No pixelwise correspondence across images • Extract features instead to obtain correspondence across images • These are numbers that summarize characteristics of each image • Spatial summary statistics are metrics that summarize spatial clustering Image feature extraction 34

Slide 35

Slide 35 text

Spatial summary statistics • Extracted to describe spatial relationship amongst cells in an image • Often separated by tumor/stroma • Univariate spatial summary statistics • Clustering of cells of one type • Bivariate spatial summary statistics • Co-expression or co-clustering of two cell types (e.g. T-cells and B-cells) • Ripley’s K function is a popular metric of spatial clustering 35

Slide 36

Slide 36 text

• K-function is a popular metric for analyzing spatial correlation • The standardized average number of neighbors of a cell within radius r • Has theoretical value of 𝜋𝑟! under complete spatial randomness (CSR) • Compare observed to theoretical value to assess clustering Ripley’s K-function 36

Slide 37

Slide 37 text

Ripley’s K effectively quantifies spatial clustering in scMI • Based on point processes • Locations of cells random variable • Gray cells: background cells • Red cells: immune cells • Goal is to quantify deviations from complete spatial randomness (CSR) • ! 𝐾 𝑟 − 𝐸!"# 𝐾 𝑟 used to predict patient survival 37

Slide 38

Slide 38 text

• Mark correlation function • Derivative of K function • Nearest-neighbor G function • Examines probability of encountering a neighboring cell • Moran’s I • Can be used to quantify continuous marker intensity values • Local and global versions • Univariate and bivariate Other spatial summary statistics 38

Slide 39

Slide 39 text

Code Interlude 3: Spatial Analysis 39

Slide 40

Slide 40 text

Acknowledgements, and thanks! Colorado SPH Biostatistics • Thao Vu • Souvik Seal • Tushar Ghosh • Debashis Ghosh Moffit Cancer Center • Brooke Fridley • Lauren Peres • Alex Soupir Vanderbilt Biostatistics • Simon Vandekar • Ruby Xiong • Coleman Harris Contact Info [email protected] juliawrobel.com github.com/julia-wrobel