Ovarian cancer-derived IL-4 promotes immunotherapy resistance.

Ovarian cancer is resistant to immunotherapy, and this is influenced by the immunosuppressed tumor microenvironment (TME) dominated by macrophages. Resistance is also affected by intratumoral heterogeneity, whose development is poorly understood. To identify regulators of ovarian cancer immunity, we employed a spatial functional genomics screen (Perturb-map), focused on receptor/ligands hypothesized to be involved in tumor-macrophage communication. Perturb-map recapitulated tumor heterogeneity and revealed that interleukin-4 (IL-4) promotes resistance to anti-PD-1. We find ovarian cancer cells are the key source of IL-4, which directs the formation of an immunosuppressive TME via macrophage control. IL-4 loss was not compensated by nearby IL-4-expressing clones, revealing short-range regulation of TME composition dictating tumor evolution. Our studies show heterogeneous TMEs can emerge from localized altered expression of cancer-derived cytokines/chemokines that establish immune-rich and immune-excluded neighborhoods, which drive clone selection and immunotherapy resistance. They also demonstrate the potential of targeting IL-4 signaling to enhance ovarian cancer response to immunotherapy.

Molecular cartography uncovers evolutionary and microenvironmental dynamics in sporadic colorectal tumors.

Colorectal cancer exhibits dynamic cellular and genetic heterogeneity during progression from precursor lesions toward malignancy. Analysis of spatial multi-omic data from 31 human colorectal specimens enabled phylogeographic mapping of tumor evolution that revealed individualized progression trajectories and accompanying microenvironmental and clonal alterations. Phylogeographic mapping ordered genetic events, classified tumors by their evolutionary dynamics, and placed clonal regions along global pseudotemporal progression trajectories encompassing the chromosomal instability (CIN+) and hypermutated (HM) pathways. Integrated single-cell and spatial transcriptomic data revealed recurring epithelial programs and infiltrating immune states along progression pseudotime. We discovered an immune exclusion signature (IEX), consisting of extracellular matrix regulators DDR1, TGFBI, PAK4, and DPEP1, that charts with CIN+ tumor progression, is associated with reduced cytotoxic cell infiltration, and shows prognostic value in independent cohorts. This spatial multi-omic atlas provides insights into colorectal tumor-microenvironment co-evolution, serving as a resource for stratification and targeted treatments.

Differential pre-malignant programs and microenvironment chart distinct paths to malignancy in human colorectal polyps.

Colorectal cancers (CRCs) arise from precursor polyps whose cellular origins, molecular heterogeneity, and immunogenic potential may reveal diagnostic and therapeutic insights when analyzed at high resolution. We present a single-cell transcriptomic and imaging atlas of the two most common human colorectal polyps, conventional adenomas and serrated polyps, and their resulting CRC counterparts. Integrative analysis of 128 datasets from 62 participants reveals adenomas arise from WNT-driven expansion of stem cells, while serrated polyps derive from differentiated cells through gastric metaplasia. Metaplasia-associated damage is coupled to a cytotoxic immune microenvironment preceding hypermutation, driven partly by antigen-presentation differences associated with tumor cell-differentiation status. Microsatellite unstable CRCs contain distinct non-metaplastic regions where tumor cells acquire stem cell properties and cytotoxic immune cells are depleted. Our multi-omic atlas provides insights into malignant progression of colorectal polyps and their microenvironment, serving as a framework for precision surveillance and prevention of CRC.

Inherited and De Novo Genetic Risk for Autism Impacts Shared Networks.

We performed a comprehensive assessment of rare inherited variation in autism spectrum disorder (ASD) by analyzing whole-genome sequences of 2,308 individuals from families with multiple affected children. We implicate 69 genes in ASD risk, including 24 passing genome-wide Bonferroni correction and 16 new ASD risk genes, most supported by rare inherited variants, a substantial extension of previous findings. Biological pathways enriched for genes harboring inherited variants represent cytoskeletal organization and ion transport, which are distinct from pathways implicated in previous studies. Nevertheless, the de novo and inherited genes contribute to a common protein-protein interaction network. We also identified structural variants (SVs) affecting non-coding regions, implicating recurrent deletions in the promoters of DLG2 and NR3C2. Loss of nr3c2 function in zebrafish disrupts sleep and social function, overlapping with human ASD-related phenotypes. These data support the utility of studying multiplex families in ASD and are available through the Hartwell Autism Research and Technology portal.

Rational Engineering of XCaMPs, a Multicolor GECI Suite for In Vivo Imaging of Complex Brain Circuit Dynamics.

To decipher dynamic brain information processing, current genetically encoded calcium indicators (GECIs) are limited in single action potential (AP) detection speed, combinatorial spectral compatibility, and two-photon imaging depth. To address this, here, we rationally engineered a next-generation quadricolor GECI suite, XCaMPs. Single AP detection was achieved within 3-10 ms of spike onset, enabling measurements of fast-spike trains in parvalbumin (PV)-positive interneurons in the barrel cortex in vivo and recording three distinct (two inhibitory and one excitatory) ensembles during pre-motion activity in freely moving mice. In vivo paired recording of pre- and postsynaptic firing revealed spatiotemporal constraints of dendritic inhibition in layer 1 in vivo, between axons of somatostatin (SST)-positive interneurons and apical tufts dendrites of excitatory pyramidal neurons. Finally, non-invasive, subcortical imaging using red XCaMP-R uncovered somatosensation-evoked persistent activity in hippocampal CA1 neurons. Thus, the XCaMPs offer a critical enhancement of solution space in studies of complex neuronal circuit dynamics. VIDEO ABSTRACT.

Precise Editing at DNA Replication Forks Enables Multiplex Genome Engineering in Eukaryotes.

We describe a multiplex genome engineering technology in Saccharomyces cerevisiae based on annealing synthetic oligonucleotides at the lagging strand of DNA replication. The mechanism is independent of Rad51-directed homologous recombination and avoids the creation of double-strand DNA breaks, enabling precise chromosome modifications at single base-pair resolution with an efficiency of >40%, without unintended mutagenic changes at the targeted genetic loci. We observed the simultaneous incorporation of up to 12 oligonucleotides with as many as 60 targeted mutations in one transformation. Iterative transformations of a complex pool of oligonucleotides rapidly produced large combinatorial genomic diversity >105. This method was used to diversify a heterologous ß-carotene biosynthetic pathway that produced genetic variants with precise mutations in promoters, genes, and terminators, leading to altered carotenoid levels. Our approach of engineering the conserved processes of DNA replication, repair, and recombination could be automated and establishes a general strategy for multiplex combinatorial genome engineering in eukaryotes.

A common NFKB1 variant detected through antibody analysis in UK Biobank predicts risk of infection and allergy.

Infectious agents contribute significantly to the global burden of diseases through both acute infection and their chronic sequelae. We leveraged the UK Biobank to identify genetic loci that influence humoral immune response to multiple infections. From 45 genome-wide association studies in 9,611 participants from UK Biobank, we identified NFKB1 as a locus associated with quantitative antibody responses to multiple pathogens, including those from the herpes, retro-, and polyoma-virus families. An insertion-deletion variant thought to affect NFKB1 expression (rs28362491), was mapped as the likely causal variant and could play a key role in regulation of the immune response. Using 121 infection- and inflammation-related traits in 487,297 UK Biobank participants, we show that the deletion allele was associated with an increased risk of infection from diverse pathogens but had a protective effect against allergic disease. We propose that altered expression of NFKB1, as a result of the deletion, modulates hematopoietic pathways and likely impacts cell survival, antibody production, and inflammation. Taken together, we show that disruptions to the tightly regulated immune processes may tip the balance between exacerbated immune responses and allergy, or increased risk of infection and impaired resolution of inflammation.

Open-source, high-throughput targeted in situ transcriptomics for developmental and tissue biology.

Multiplexed spatial profiling of mRNAs has recently gained traction as a tool to explore the cellular diversity and the architecture of tissues. We propose a sensitive, open-source, simple and flexible method for the generation of in situ expression maps of hundreds of genes. We use direct ligation of padlock probes on mRNAs, coupled with rolling circle amplification and hybridization-based in situ combinatorial barcoding, to achieve high detection efficiency, high-throughput and large multiplexing. We validate the method across a number of species and show its use in combination with orthogonal methods such as antibody staining, highlighting its potential value for developmental and tissue biology studies. Finally, we provide an end-to-end computational workflow that covers the steps of probe design, image processing, data extraction, cell segmentation, clustering and annotation of cell types. By enabling easier access to high-throughput spatially resolved transcriptomics, we hope to encourage a diversity of applications and the exploration of a wide range of biological questions.

StratoLAMP: Label-free, multiplex digital loop-mediated isothermal amplification based on visual stratification of precipitate.

Multiplex, digital nucleic acid detections have important biomedical applications, but the multiplexity of existing methods is predominantly achieved using fluorescent dyes or probes, making the detection complicated and costly. Here, we present the StratoLAMP for label-free, multiplex digital loop-mediated isothermal amplification based on visual stratification of the precipitate byproduct. The StratoLAMP designates two sets of primers with different concentrations to achieve different precipitate yields when amplifying different nucleic acid targets. In the detection, deep learning image analysis is used to stratify the precipitate within each droplet and determine the encapsulated targets for nucleic acid quantification. We investigated the effect of the amplification reagents and process on the precipitate generation and optimized the assay conditions. We then implemented a deep-learning image analysis pipeline for droplet detection, achieving an overall accuracy of 94.3%. In the application, the StratoLAMP successfully achieved the simultaneous quantification of two nucleic acid targets with high accuracy. By eliminating the need for fluorescence, StratoLAMP represents a unique concept toward label-free, multiplex nucleic acid assays and an analytical tool with great cost-effectiveness.

scMLC: an accurate and robust multiplex community detection method for single-cell multi-omics data.

Clustering cells based on single-cell multi-modal sequencing technologies provides an unprecedented opportunity to create high-resolution cell atlas, reveal cellular critical states and study health and diseases. However, effectively integrating different sequencing data for cell clustering remains a challenging task. Motivated by the successful application of Louvain in scRNA-seq data, we propose a single-cell multi-modal Louvain clustering framework, called scMLC, to tackle this problem. scMLC builds multiplex single- and cross-modal cell-to-cell networks to capture modal-specific and consistent information between modalities and then adopts a robust multiplex community detection method to obtain the reliable cell clusters. In comparison with 15 state-of-the-art clustering methods on seven real datasets simultaneously measuring gene expression and chromatin accessibility, scMLC achieves better accuracy and stability in most datasets. Synthetic results also indicate that the cell-network-based integration strategy of multi-omics data is superior to other strategies in terms of generalization. Moreover, scMLC is flexible and can be extended to single-cell sequencing data with more than two modalities.

Promises and Challenges of populational Proteomics in Health and Disease.

Advances in proteomic assay technologies have significantly increased coverage and throughput, enabling recent increases in the number of large-scale population-based proteomic studies of human plasma and serum. Improvements in multiplexed protein assays have facilitated the quantification of thousands of proteins over a large dynamic range, a key requirement for detecting the lowest-ranging, and potentially the most disease-relevant, blood-circulating proteins. In this perspective, we examine how populational proteomic datasets in conjunction with other concurrent omic measures can be leveraged to better understand the genomic and non-genomic correlates of the soluble proteome, constructing biomarker panels for disease prediction, among others. Mass spectrometry workflows are discussed as they are becoming increasingly competitive with affinity-based array platforms in terms of speed, cost, and proteome coverage due to advances in both instrumentation and workflows. Despite much success, there remain considerable challenges such as orthogonal validation and absolute quantification. We also highlight emergent challenges associated with study design, analytical considerations, and data integration as population-scale studies are run in batches and may involve longitudinal samples collated over many years. Lastly, we take a look at the future of what the nascent next-generation proteomic technologies might provide to the analysis of large sets of blood samples, as well as the difficulties in designing large-scale studies that will likely require participation from multiple and complex funding sources and where data sharing, study designs, and financing must be solved.

The contributions of rare inherited and polygenic risk to ASD in multiplex families.

Autism spectrum disorder (ASD) has a complex genetic architecture involving contributions from both de novo and inherited variation. Few studies have been designed to address the role of rare inherited variation or its interaction with common polygenic risk in ASD. Here, we performed whole-genome sequencing of the largest cohort of multiplex families to date, consisting of 4,551 individuals in 1,004 families having two or more autistic children. Using this study design, we identify seven previously unrecognized ASD risk genes supported by a majority of rare inherited variants, finding support for a total of 74 genes in our cohort and a total of 152 genes after combined analysis with other studies. Autistic children from multiplex families demonstrate an increased burden of rare inherited protein-truncating variants in known ASD risk genes. We also find that ASD polygenic score (PGS) is overtransmitted from nonautistic parents to autistic children who also harbor rare inherited variants, consistent with combinatorial effects in the offspring, which may explain the reduced penetrance of these rare variants in parents. We also observe that in addition to social dysfunction, language delay is associated with ASD PGS overtransmission. These results are consistent with an additive complex genetic risk architecture of ASD involving rare and common variation and further suggest that language delay is a core biological feature of ASD.

Silver electroceutical technology to treat sarcopenia.

While the world is rapidly transforming into a superaging society, pharmaceutical approaches to treat sarcopenia have hitherto not been successful due to their insufficient efficacy and failure to specifically target skeletal muscle cells (skMCs). Although electrical stimulation (ES) is emerging as an alternative intervention, its efficacy toward treating sarcopenia remains unexplored. In this study, we demonstrate a silver electroceutical technology with the potential to treat sarcopenia. First, we developed a high-throughput ES screening platform that can simultaneously stimulate 15 independent conditions, while utilizing only a small number of human-derived primary aged/young skMCs (hAskMC/hYskMC). The in vitro screening showed that specific ES conditions induced hypertrophy and rejuvenation in hAskMCs, and the optimal ES frequency in hAskMCs was different from that in hYskMCs. When applied to aged mice in vivo, specific ES conditions improved the prevalence and thickness of Type IIA fibers, along with biomechanical attributes, toward a younger skMC phenotype. This study is expected to pave the way toward an electroceutical treatment for sarcopenia with minimal side effects and help realize personalized bioelectronic medicine.

Molecular basis and functional consequences of the interaction between the base excision repair DNA glycosylase NEIL1 and RPA.

NEIL1 is a DNA glycosylase that recognizes and initiates base excision repair of oxidized bases. The ubiquitous ssDNA binding scaffolding protein, replication protein A (RPA), modulates NEIL1 activity in a manner that depends on DNA structure. Interaction between NEIL1 and RPA has been reported, but the molecular basis of this interaction has yet to be investigated. Using a combination of NMR spectroscopy and isothermal titration calorimetry (ITC), we show that NEIL1 interacts with RPA through two contact points. An interaction with the RPA32C protein recruitment domain was mapped to a motif in the common interaction domain (CID) of NEIL1 and a dissociation constant (Kd) of 200 nM was measured. A substantially weaker secondary interaction with the tandem RPA70AB ssDNA binding domains was also mapped to the CID. Together these two contact points reveal NEIL1 has a high overall affinity (Kd ∼ 20 nM) for RPA. A homology model of the complex of RPA32C with the NEIL1 RPA binding motif in the CID was generated and used to design a set of mutations in NEIL1 to disrupt the interaction, which was confirmed by ITC. The mutant NEIL1 remains catalytically active against a thymine glycol lesion in duplex DNA in vitro. Testing the functional effect of disrupting the NEIL1-RPA interaction in vivo using a Fluorescence Multiplex-Host Cell Reactivation (FM-HCR) reporter assay revealed an unexpected role for NEIL1 in nucleotide excision repair. These findings are discussed in the context of the role of NEIL1 in replication-associated repair.

Cell-based optimization and characterization of genetically encoded location-based biosensors for Cdc42 or Rac activity.

Rac (herein referring to the Rac family) and Cdc42 are Rho GTPases that regulate the formation of lamellipoda and filopodia, and are therefore crucial in processes such as cell migration. Relocation-based biosensors for Rac and Cdc42 have not been characterized well in terms of their specificity or affinity. In this study, we identify relocation sensor candidates for both Rac and Cdc42. We compared their (1) ability to bind the constitutively active Rho GTPases, (2) specificity for Rac and Cdc42, and (3) relocation efficiency in cell-based assays. Subsequently, the relocation efficiency was improved by a multi-domain approach. For Rac1, we found a sensor candidate with low relocation efficiency. For Cdc42, we found several sensors with sufficient relocation efficiency and specificity. These optimized sensors enable the wider application of Rho GTPase relocation sensors, which was showcased by the detection of local endogenous Cdc42 activity at assembling invadopodia. Moreover, we tested several fluorescent proteins and HaloTag for their influence on the recruitment efficiency of the Rho location sensor, to find optimal conditions for a multiplexing experiment. This characterization and optimization of relocation sensors will broaden their application and acceptance.

Detection of dendritic cell subsets in the tumor microenvironment by multiplex immunohistochemistry.

Dendritic cells (DCs) are essential in antitumor immunity. In humans, three main DC subsets are defined: two types of conventional DCs (cDC1s and cDC2s) and plasmacytoid DCs (pDCs). To study DC subsets in the tumor microenvironment (TME), it is important to correctly identify them in tumor tissues. Tumor-derived DCs are often analyzed in cell suspensions in which spatial information about DCs which can be important to determine their function within the TME is lost. Therefore, we developed the first standardized and optimized multiplex immunohistochemistry panel, simultaneously detecting cDC1s, cDC2s, and pDCs within their tissue context. We report on this panel's development, validation, and quantitative analysis. A multiplex immunohistochemistry panel consisting of CD1c, CD303, X-C motif chemokine receptor 1, CD14, CD19, a tumor marker, and DAPI was established. The ImmuNet machine learning pipeline was trained for the detection of DC subsets. The performance of ImmuNet was compared with conventional cell phenotyping software. Ultimately, frequencies of DC subsets within several tumors were defined. In conclusion, this panel provides a method to study cDC1s, cDC2s, and pDCs in the spatial context of the TME, which supports unraveling their specific roles in antitumor immunity.

Tissue niches and immunopathology through the lens of spatial tissue profiling techniques.

Spatial organization plays a fundamental role in biology, influencing the function of biological structures at various levels. The immune system, in particular, relies on the orchestrated interactions of immune cells with their microenvironment to mount protective or pathogenic immune responses. The COVID-19 pandemic has underscored the significance of studying immunity within target organs to understand disease progression and severity. To achieve this, multiplex histology and spatial transcriptomics have proven indispensable in providing a spatial context to protein and gene expression patterns. By combining these techniques, researchers gain a more comprehensive understanding of the complex interactions at the cellular and molecular level in distinct tissue niches, key functional units modulating health and disease. In this review, we discuss recent advances in spatial tissue profiling techniques, highlighting their advantages over traditional histopathology studies. The insights gained from these approaches have the potential to revolutionize the diagnosis and treatment of various diseases including cancer, autoimmune disorders, and infectious diseases. However, we also acknowledge their challenges and limitations. Despite these, spatial tissue profiling offers promising opportunities to improve our understanding of how tissue niches direct regional immunity, and their relevance in tissue immunopathology, as a basis for novel therapeutic strategies and personalized medicine.

Bead-by-bead normalization of single antigen assays: A necessary step for accurate detection of weak anti-HLA antibodies.

Ascertaining the presence of weakly positive anti-HLA donor-specific antibodies (DSA) in organ transplantation with multiplex single antigen beads assays may be challenging despite their high sensitivity due to technical variability issues. Through extensive datasets of Next-Generation Sequencing HLA typings and single antigen analyses, we reassessed the mean fluorescence intensity (MFI) positivity threshold of the assay to enhance accuracy. By showing that some beads were more prone to false positivity than others, we propose a nuanced approach that accounts for nonspecific intrinsic reactivities at the HLA antigen level, that is, on a bead-by-bead basis, as it enhances assay precision and reliability. This is substantiated by a comprehensive statistical analysis of MFI values and the implementation of the determination of a "Quantile Adjusted Threshold 500" (QAT500) value for each bead. Applied to DSA detection during patients' follow-up, this approach discriminated better and earlier low-strength DSA that would later raise their MFI above the clinically relevant threshold of 3000. Moving from a subjective interpretation to a more objective and precise methodology allows for standardizing HLA antibody and DSA detection. The study emphasizes the need for further research with real clinical data to validate and refine this approach.

Cooperative driver pathways discovery by multiplex network embedding.

Cooperative driver pathways discovery helps researchers to study the pathogenesis of cancer. However, most discovery methods mainly focus on genomics data, and neglect the known pathway information and other related multi-omics data; thus they cannot faithfully decipher the carcinogenic process. We propose CDPMiner (Cooperative Driver Pathways Miner) to discover cooperative driver pathways by multiplex network embedding, which can jointly model relational and attribute information of multi-type molecules. CDPMiner first uses the pathway topology to quantify the weight of genes in different pathways, and optimizes the relations between genes and pathways. Then it constructs an attributed multiplex network consisting of micro RNAs, long noncoding RNAs, genes and pathways, embeds the network through deep joint matrix factorization to mine more essential information for pathway-level analysis and reconstructs the pathway interaction network. Finally, CDPMiner leverages the reconstructed network and mutation data to define the driver weight between pathways to discover cooperative driver pathways. Experimental results on Breast invasive carcinoma and Stomach adenocarcinoma datasets show that CDPMiner can effectively fuse multi-omics data to discover more driver pathways, which indeed cooperatively trigger cancers and are valuable for carcinogenesis analysis. Ablation study justifies CDPMiner for a more comprehensive analysis of cancer by fusing multi-omics data.

Model-based evaluation of spatiotemporal data reduction methods with unknown ground truth through optimal visualization and interpretability metrics.

Optimizing and benchmarking data reduction methods for dynamic or spatial visualization and interpretation (DSVI) face challenges due to many factors, including data complexity, lack of ground truth, time-dependent metrics, dimensionality bias and different visual mappings of the same data. Current studies often focus on independent static visualization or interpretability metrics that require ground truth. To overcome this limitation, we propose the MIBCOVIS framework, a comprehensive and interpretable benchmarking and computational approach. MIBCOVIS enhances the visualization and interpretability of high-dimensional data without relying on ground truth by integrating five robust metrics, including a novel time-ordered Markov-based structural metric, into a semi-supervised hierarchical Bayesian model. The framework assesses method accuracy and considers interaction effects among metric features. We apply MIBCOVIS using linear and nonlinear dimensionality reduction methods to evaluate optimal DSVI for four distinct dynamic and spatial biological processes captured by three single-cell data modalities: CyTOF, scRNA-seq and CODEX. These data vary in complexity based on feature dimensionality, unknown cell types and dynamic or spatial differences. Unlike traditional single-summary score approaches, MIBCOVIS compares accuracy distributions across methods. Our findings underscore the joint evaluation of visualization and interpretability, rather than relying on separate metrics. We reveal that prioritizing average performance can obscure method feature performance. Additionally, we explore the impact of data complexity on visualization and interpretability. Specifically, we provide optimal parameters and features and recommend methods, like the optimized variational contractive autoencoder, for targeted DSVI for various data complexities. MIBCOVIS shows promise for evaluating dynamic single-cell atlases and spatiotemporal data reduction models.