Single-cell sequencing and proteomics have been critical for the study of human disease. However, highly multiplexed microscopy has revolutionized spatial biology by measuring cell expression from ~50 proteins while maintaining spatial locations of cells. This presents unique computational challenges; acquiring manual annotations across so many image channels is challenging, therefore supervised learning methods for classification are undesirable. To overcome this limitation we have developed a decision-tree classifier for the multiclass annotation of renal cells that is analogous to well-established flow cytometry-based cell analyses. We demonstrate this method of cell annotation in a dataset of 54 kidney biopsies from patients with three different pathologies: 25 with lupus nephritis, 23 with renal allograft rejection, and six with non-autoimmune conditions. Biopsies were iteratively stained and imaged using the PhenoCycler protocol to acquire high-resolution, full-section images with a 43-marker panel. Nucleus segmentation was performed using Cellpose2.0 and whole cell segmentation was approximated by dilating the nucleus masks. In our decision tree, cells are sequentially sorted into marker-negative and marker-positive populations using their mean fluorescence intensity (MFI). A multi-Otsu threshold, in conjunction with manual spot checking, is used for determining the optimal MFI threshold for each branching of the decision tree. Marker order is based upon well-established, hierarchical expression of immunological cell markers created in consultation with expert immunologists. We have further developed another algorithm to probe microtubule organizing center polarization, an important immunologic behavior. Ultimately, we were able to assign biologically-defined cell classes to 1.59 million of 2.19 million cells captured in tissue.
Lupus nephritis (LN) is a severe manifestation of systemic lupus erythematosus, with up to 30% of LN patients progressing to end-stage kidney disease within ten years of diagnosis. Spatial relationships between specific types of immune cells and kidney structures hold valuable information clinically and biologically. Thus, we develop a modular computational pipeline to analyze the spatially resolved molecular features from high-plex immunofluorescence imaging data. Here, we present three modules of the pipeline, with the goal of achieving multiclass segmentation of renal cells and structures.
The goal of this work is to reduce the complexity of cell and cell neighborhood annotations in studies for spatial immunity. Specifically, we use a method inspired by spectral angle mapping to collapse multichannel images into class-level representations. We will demonstrate that these class maps assist in characterizing immune cell infiltration in renal pathologies.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.