SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis.

MOTIVATION: Spatial omics data demand computational analysis but many analysis tools have computational resource requirements that increase with the number of cells analyzed. This presents scalability challenges as researchers use spatial omics technologies to profile millions of cells. RESULTS: To enhance the scalability of spatial omics data analysis, we developed a rasterization preprocessing framework called SEraster that aggregates cellular information into spatial pixels. We apply SEraster to both real and simulated spatial omics data prior to spatial variable gene expression analysis to demonstrate that such preprocessing can reduce computational resource requirements while maintaining high performance, including as compared to other down-sampling approaches. We further integrate SEraster with existing analysis tools to characterize cell-type spatial co-enrichment across length scales. Finally, we apply SEraster to enable analysis of a mouse pup spatial omics dataset with over a million cells to identify tissue-level and cell-type-specific spatially variable genes as well as spatially co-enriched cell types that recapitulate expected organ structures. AVAILABILITY AND IMPLEMENTATION: SEraster is implemented as an R package on GitHub (https://github.com/JEFworks-Lab/SEraster) with additional tutorials at https://JEF.works/SEraster.

Gene count normalization in single-cell imaging-based spatially resolved transcriptomics.

BACKGROUND: Recent advances in imaging-based spatially resolved transcriptomics (im-SRT) technologies now enable high-throughput profiling of targeted genes and their locations in fixed tissues. Normalization of gene expression data is often needed to account for technical factors that may confound underlying biological signals. RESULTS: Here, we investigate the potential impact of different gene count normalization methods with different targeted gene panels in the analysis and interpretation of im-SRT data. Using different simulated gene panels that overrepresent genes expressed in specific tissue regions or cell types, we demonstrate how normalization methods based on detected gene counts per cell differentially impact normalized gene expression magnitudes in a region- or cell type-specific manner. We show that these normalization-induced effects may reduce the reliability of downstream analyses including differential gene expression, gene fold change, and spatially variable gene analysis, introducing false positive and false negative results when compared to results obtained from gene panels that are more representative of the gene expression of the tissue's component cell types. These effects are not observed with normalization approaches that do not use detected gene counts for gene expression magnitude adjustment, such as with cell volume or cell area normalization. CONCLUSIONS: We recommend using non-gene count-based normalization approaches when feasible and evaluating gene panel representativeness before using gene count-based normalization methods if necessary. Overall, we caution that the choice of normalization method and gene panel may impact the biological interpretation of the im-SRT data.

Gene count normalization in single-cell imaging-based spatially resolved transcriptomics.

Recent advances in imaging-based spatially resolved transcriptomics (im-SRT) technologies now enable high-throughput profiling of targeted genes and their locations in fixed tissues. Normalization of gene expression data is often needed to account for technical factors that may confound underlying biological signals. Here, we investigate the potential impact of different gene count normalization methods with different targeted gene panels in the analysis and interpretation of im-SRT data. Using different simulated gene panels that overrepresent genes expressed in specific tissue regions or cell types, we demonstrate how normalization methods based on detected gene counts per cell differentially impact normalized gene expression magnitudes in a region- or cell type-specific manner. We show that these normalization-induced effects may reduce the reliability of downstream analyses including differential gene expression, gene fold change, and spatially variable gene analysis, introducing false positive and false negative results when compared to results obtained from gene panels that are more representative of the gene expression of the tissue's component cell types. These effects are not observed with normalization approaches that do not use detected gene counts for gene expression magnitude adjustment, such as with cell volume or cell area normalization. We recommend using non-gene count-based normalization approaches when feasible and evaluating gene panel representativeness before using gene count-based normalization methods if necessary. Overall, we caution that the choice of normalization method and gene panel may impact the biological interpretation of the im-SRT data.

STalign: Alignment of spatial transcriptomics data using diffeomorphic metric mapping.

Spatial transcriptomics (ST) technologies enable high throughput gene expression characterization within thin tissue sections. However, comparing spatial observations across sections, samples, and technologies remains challenging. To address this challenge, we develop STalign to align ST datasets in a manner that accounts for partially matched tissue sections and other local non-linear distortions using diffeomorphic metric mapping. We apply STalign to align ST datasets within and across technologies as well as to align ST datasets to a 3D common coordinate framework. We show that STalign achieves high gene expression and cell-type correspondence across matched spatial locations that is significantly improved over landmark-based affine alignments. Applying STalign to align ST datasets of the mouse brain to the 3D common coordinate framework from the Allen Brain Atlas, we highlight how STalign can be used to lift over brain region annotations and enable the interrogation of compositional heterogeneity across anatomical structures. STalign is available as an open-source Python toolkit at https://github.com/JEFworks-Lab/STalign and as Supplementary Software with additional documentation and tutorials available at https://jef.works/STalign .

Alignment of spatial transcriptomics data using diffeomorphic metric mapping.

Spatial transcriptomics (ST) technologies enable high throughput gene expression characterization within thin tissue sections. However, comparing spatial observations across sections, samples, and technologies remains challenging. To address this challenge, we developed STalign to align ST datasets in a manner that accounts for partially matched tissue sections and other local non-linear distortions using diffeomorphic metric mapping. We apply STalign to align ST datasets within and across technologies as well as to align ST datasets to a 3D common coordinate framework. We show that STalign achieves high gene expression and cell-type correspondence across matched spatial locations that is significantly improved over landmark-based affine alignments. Applying STalign to align ST datasets of the mouse brain to the 3D common coordinate framework from the Allen Brain Atlas, we highlight how STalign can be used to lift over brain region annotations and enable the interrogation of compositional heterogeneity across anatomical structures. STalign is available as an open-source Python toolkit at https://github.com/JEFworks-Lab/STalign and as supplementary software with additional documentation and tutorials available at https://jef.works/STalign.

Nanoparticle-based modulation of CD4+ T cell effector and helper functions enhances adoptive immunotherapy.

Helper (CD4+) T cells perform direct therapeutic functions and augment responses of cells such as cytotoxic (CD8+) T cells against a wide variety of diseases and pathogens. Nevertheless, inefficient synthetic technologies for expansion of antigen-specific CD4+ T cells hinders consistency and scalability of CD4+ T cell-based therapies, and complicates mechanistic studies. Here we describe a nanoparticle platform for ex vivo CD4+ T cell culture that mimics antigen presenting cells (APC) through display of major histocompatibility class II (MHC II) molecules. When combined with soluble co-stimulation signals, MHC II artificial APCs (aAPCs) expand cognate murine CD4+ T cells, including rare endogenous subsets, to induce potent effector functions in vitro and in vivo. Moreover, MHC II aAPCs provide help signals that enhance antitumor function of aAPC-activated CD8+ T cells in a mouse tumor model. Lastly, human leukocyte antigen class II-based aAPCs expand rare subsets of functional, antigen-specific human CD4+ T cells. Overall, MHC II aAPCs provide a promising approach for harnessing targeted CD4+ T cell responses.

Adequacy of samples obtained via percutaneous core-needle rebiopsy for EGFR T790M molecular analysis in patients with non-small cell lung cancer following acquired resistance to first-line therapy: A systematic review and meta-analysis.

MICRO ABSTRACT: Rebiopsies characterizing resistance mutations in patients with non-small cell lung cancer (NSCLC) can guide personalized medicine and improve overall survival rates. In this systematic review, we examine the suitability of percutaneous core-needle biopsy (PT-CNB) to obtain adequate samples for molecular characterization of the acquired resistance mutation T790M. This review provides evidence that PT-CNB can obtain samples with high adequacy, with a mutation detection rate that is in accordance with prior literature. BACKGROUND: Non-small cell lung cancer (NSCLC) comprises 85% of all lung cancers and has seen improved survival rates with the rise of personalized medicine. Resistance mutations to first-line therapies, such as T790M, however, render first-line therapies ineffective. Rebiopsies characterizing resistance mutations inform therapeutic decisions, which result in prolonged survival. Given the high efficacy of percutaneous core-needle biopsy (PT-CNB), we conducted the first systematic review to analyze the ability of PT-CNB to obtain samples of high adequacy in order to characterize the acquired resistance mutation T790M in patients with NSCLC. METHODS: We performed a comprehensive literature search across PubMed, Embase, and CENTRAL. Search terms related to "NSCLC," "rebiopsy," and "PT-CNB" were used to obtain results. We included all prospective and retrospective studies that satisfied our inclusion and exclusion criteria. A random effects model was utilized to pool adequacy and detection rates of the chosen articles. We performed a systematic review, meta-analysis, and meta-regression to investigate the adequacy and T790M detection rates of samples obtained via PT-CNB. RESULTS: Out of the 173 studies initially identified, 5 studies met the inclusion and exclusion criteria and were chosen for our final cohort of 436 patients for meta-analysis. The pooled adequacy rate of samples obtained via PT-CNB was 86.92% (95% CI: [79.31%, 92.0%]) and the pooled T790M detection rate was 46.0% (95% CI: [26.6%, 66.7%]). There was considerable heterogeneity among studies (I2 > 50%) in both adequacy and T790M detection rates. CONCLUSION: PT-CNB can obtain adequate samples for T790M molecular characterization in NSCLC lung cancer patients. Additional prospective studies are needed to corroborate the results in this review.