Consensus tissue domain detection in spatial omics data using multiplex image labeling with regional morphology (MILWRM).

Spatially resolved molecular assays provide high dimensional genetic, transcriptomic, proteomic, and epigenetic information in situ and at various resolutions. Pairing these data across modalities with histological features enables powerful studies of tissue pathology in the context of an intact microenvironment and tissue structure. Increasing dimensions across molecular analytes and samples require new data science approaches to functionally annotate spatially resolved molecular data. A specific challenge is data-driven cross-sample domain detection that allows for analysis within and between consensus tissue compartments across high volumes of multiplex datasets stemming from tissue atlasing efforts. Here, we present MILWRM (multiplex image labeling with regional morphology)-a Python package for rapid, multi-scale tissue domain detection and annotation at the image- or spot-level. We demonstrate MILWRM's utility in identifying histologically distinct compartments in human colonic polyps, lymph nodes, mouse kidney, and mouse brain slices through spatially-informed clustering in two different spatial data modalities from different platforms. We used tissue domains detected in human colonic polyps to elucidate the molecular distinction between polyp subtypes, and explored the ability of MILWRM to identify anatomical regions of the brain tissue and their respective distinct molecular profiles.

Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data.

Advances in multiplexed single-cell immunofluorescence (mIF) and multiplex immunohistochemistry (mIHC) imaging technologies have enabled the analysis of cell-to-cell spatial relationships that promise to revolutionize our understanding of tissue-based diseases and autoimmune disorders. Multiplex images are collected as multichannel TIFF files; then denoised, segmented to identify cells and nuclei, normalized across slides with protein markers to correct for batch effects, and phenotyped; and then tissue composition and spatial context at the cellular level are analyzed. This chapter discusses methods and software infrastructure for image processing and statistical analysis of mIF/mIHC data.

mxnorm: An R Package to Normalize Multiplexed Imaging Data.

Multiplexed imaging is an emerging single-cell assay that can be used to understand and analyze complex processes in tissue-based cancers, autoimmune disorders, and more. These imaging technologies, which include co-detection by indexing (CODEX), multiplexed ion beam imaging (MIBI), and multiplexed immunofluorescence imaging (MxIF), provide detailed information about spatial interactions between cells (Angelo et al., 2014; Gerdes et al., 2013; Goltsev et al., 2018). Multiplexed imaging experiments generate data across hundreds of slides and images, often resulting in terabytes of complex data to analyze through imaging analysis pipelines. Methods are rapidly developing to improve particular parts of the pipeline, including software packages in R and Python like spatialTime, imcRtools, MCMICR0, and Squidpy (Creed et al., 2021; Palla et al., 2021; Schapiro et al., 2021; Windhager et al., 2021). An important, but understudied component of this pipeline is the analysis of technical variation within this complex data source - intensity normalization is one way to remove this technical variability. The combination of disparate pre-processing pipelines, imaging variables, optical effects, and within-slide dependencies create batch and slide effects that can be reduced via normalization methods. Current state-of-the-art methods vary heavily across research labs and image acquisition platforms, without one singular method that is uniformly robust - optimal statistical methods seek to improve similarity across images and slides by removing this technical variability while maintaining the underlying biological signal in the data. mxnorm is open-source software built with R and S3 methods that implements, evaluates, and visualizes normalization techniques for multiplexed imaging data. Extending methodology described in Harris et al. (2022), we intend to set a foundation for the evaluation of multiplexed imaging normalization methods in R. This easily allows users to extend normalization methods into the field, and provides a robust evaluation framework to measure both technical variability and the efficacy of various normalization methods. One key component of the R package is the ability to supply user-defined normalization methods and thresholding algorithms to assess normalization in multiplexed imaging data. Core features, usage details, and extensive tutorials are available in the package documentation and vignette on CRAN and the software repository.

Trust as a Predictor of Patient Perceptions Regarding Overlapping Surgery and Trainee Independence.

OBJECTIVES: To examine opinions on trainee independence and attending presence among a cross-section of the general population and explore how perceptions of trust, past experiences, and demographics interacted with comfort consenting to these surgical scenarios. STUDY DESIGN: Mixed-methods METHODS: Based on prior qualitative analysis, we designed a survey of patient preferences and values that focused on trust in healthcare practitioners and processes, which also included comfort ratings of three surgical scenarios (including overlapping surgery). The survey was administered to a sample from the general public using Mechanical Turk. We identified discreet domains of trust and examined the association of responses to these domains with comfort ratings, prior healthcare experiences, and demographics. RESULTS: We analyzed 225 surveys and identified four patient subgroups based on responses to the surgical scenarios. Subjects that were more comfortable with overlapping surgery were more trusting of trainees and delegation by the attending. Past experiences in healthcare (positive and negative) were associated with multiple domains of trust (in trainees, surgeons, and the healthcare system). Demographics were not predictive of trust responses or comfort ratings. CONCLUSION: Patients express varying degrees of comfort with overlapping surgery, and this is not associated with demographics. Past negative experiences have an impact on trust in the healthcare system overall, and trust in trainees specifically predicts comfort with attending absence from the operating room. Efforts to increase patient comfort with overlapping surgery and surgical training should include strategies to address past negative experiences and foster trust in trainees and the delegation process. LEVEL OF EVIDENCE: IV Laryngoscope, 130:2728-2735, 2020.