Coordinated chemokine expression defines macrophage subsets across tissues.

Lung-resident macrophages, which include alveolar macrophages and interstitial macrophages (IMs), exhibit a high degree of diversity, generally attributed to different activation states, and often complicated by the influx of monocytes into the pool of tissue-resident macrophages. To gain a deeper insight into the functional diversity of IMs, here we perform comprehensive transcriptional profiling of resident IMs and reveal ten distinct chemokine-expressing IM subsets at steady state and during inflammation. Similar IM subsets that exhibited coordinated chemokine signatures and differentially expressed genes were observed across various tissues and species, indicating conserved specialized functional roles. Other macrophage types shared specific IM chemokine profiles, while also presenting their own unique chemokine signatures. Depletion of CD206hi IMs in Pf4creR26EYFP+DTR and Pf4creR26EYFPCx3cr1DTR mice led to diminished inflammatory cell recruitment, reduced tertiary lymphoid structure formation and fewer germinal center B cells in models of allergen- and infection-driven inflammation. These observations highlight the specialized roles of IMs, defined by their coordinated chemokine production, in regulating immune cell influx and organizing tertiary lymphoid tissue architecture.

Resident memory CD8+ T cells in regional lymph nodes mediate immunity to metastatic melanoma.

The nature of the anti-tumor immune response changes as primary tumors progress and metastasize. We investigated the role of resident memory (Trm) and circulating memory (Tcirm) cells in anti-tumor responses at metastatic locations using a mouse model of melanoma-associated vitiligo. We found that the transcriptional characteristics of tumor-specific CD8+ T cells were defined by the tissue of occupancy. Parabiosis revealed that tumor-specific Trm and Tcirm compartments persisted throughout visceral organs, but Trm cells dominated lymph nodes (LNs). Single-cell RNA-sequencing profiles of Trm cells in LN and skin were distinct, and T cell clonotypes that occupied both tissues were overwhelmingly maintained as Trm in LNs. Whereas Tcirm cells prevented melanoma growth in the lungs, Trm afforded long-lived protection against melanoma seeding in LNs. Expanded Trm populations were also present in melanoma-involved LNs from patients, and their transcriptional signature predicted better survival. Thus, tumor-specific Trm cells persist in LNs, restricting metastatic cancer.

Identification of Spatial Proteomic Signatures of Colon Tumor Metastasis: A Digital Spatial Profiling Approach.

Over 150,000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually >50,000 individuals are estimated to die of CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. CRC tumors are removed en bloc with surrounding vasculature and lymphatics. Examination of regional lymph nodes at the time of surgical resection is essential for prognostication. Developing alternative approaches to indirectly assess recurrence risk would have utility in cases where lymph node yield is incomplete or inadequate. Spatially dependent, immune cell-specific (eg, tumor-infiltrating lymphocytes), proteomic, and transcriptomic expression patterns inside and around the tumor-the tumor immune microenvironment-can predict nodal/distant metastasis and probe the coordinated immune response from the primary tumor site. The comprehensive characterization of tumor-infiltrating lymphocytes and other immune infiltrates is possible using highly multiplexed spatial omics technologies, such as the GeoMX Digital Spatial Profiler. In this study, machine learning and differential co-expression analyses helped identify biomarkers from Digital Spatial Profiler-assayed protein expression patterns inside, at the invasive margin, and away from the tumor, associated with extracellular matrix remodeling (eg, granzyme B and fibronectin), immune suppression (eg, forkhead box P3), exhaustion and cytotoxicity (eg, CD8), Programmed death ligand 1-expressing dendritic cells, and neutrophil proliferation, among other concomitant alterations. Further investigation of these biomarkers may reveal independent risk factors of CRC metastasis that can be formulated into low-cost, widely available assays.

Natural Antibodies Alert the Adaptive Immune System of the Presence of Transformed Cells in Early Tumorigenesis.

Recent studies have revealed a critical role for natural Abs (NAbs) in antitumor immune responses. However, the role of NAbs in cancer immunosurveillance remains unexplored, mainly because of the lack of in vivo models that mimic the early recognition and elimination of transforming cells. In this article, we propose a role for NAbs in alerting the immune system against precancerous neoantigen-expressing cells immediately after they escape intrinsic tumor suppression mechanisms. We identify four distinct reproducible, trackable, MHC-matched neoantigen-expressing cell models that do not form tumors as the end point. This amplified readout in the critical window prior to tumor formation allows investigation of new mediators of cancer immunosurveillance. We found that neoantigen-expressing cells adoptively transferred in NAb-deficient mice persisted, whereas they were eliminated in wild-type mice, indicating that the circulating NAb repertoire alerts the immune system to the presence of transformed cells. Moreover, immunity is mounted against immunogenic and nonimmunogenic neoantigens contained in the NAb-tagged cells, regardless of whether the NAb directly recognizes the neoantigens. Beyond these neoantigen-expressing model systems, we observed a significantly greater tumor burden in chemically and virally induced tumor models in NAb-deficient mice compared with wild-type mice. Restoration of the NAb repertoire in NAb-deficient mice elicited the recognition and elimination of neoantigen-expressing cells and cancer. These data show that NAbs are required and sufficient for elimination of transformed cells early in tumorigenesis. These models can now be used to investigate how NAbs stimulate immunity via recognition receptors to eliminate precancerous cells.

Let-7b-5p in vesicles secreted by human airway cells reduces biofilm formation and increases antibiotic sensitivity of P. aeruginosa.

Pseudomonas aeruginosa is an opportunistic pathogen that forms antibiotic-resistant biofilms, which facilitate chronic infections in immunocompromised hosts. We have previously shown that P. aeruginosa secretes outer-membrane vesicles that deliver a small RNA to human airway epithelial cells (AECs), in which it suppresses the innate immune response. Here, we demonstrate that interdomain communication through small RNA-containing membrane vesicles is bidirectional and that microRNAs (miRNAs) in extracellular vesicles (EVs) secreted by human AECs regulate protein expression, antibiotic sensitivity, and biofilm formation by P. aeruginosa Specifically, human EVs deliver miRNA let-7b-5p to P. aeruginosa, which systematically decreases the abundance of proteins essential for biofilm formation, including PpkA and ClpV1-3, and increases the ability of beta-lactam antibiotics to reduce biofilm formation by targeting the beta-lactamase AmpC. Let-7b-5p is bioinformatically predicted to target not only PpkA, ClpV1, and AmpC in P. aeruginosa but also the corresponding orthologs in Burkholderia cenocepacia, another notorious opportunistic lung pathogen, suggesting that the ability of let-7b-5p to reduce biofilm formation and increase beta-lactam sensitivity is not limited to P. aeruginosa Here, we provide direct evidence for transfer of miRNAs in EVs secreted by eukaryotic cells to a prokaryote, resulting in subsequent phenotypic alterations in the prokaryote as a result of this interdomain communication. Since let-7-family miRNAs are in clinical trials to reduce inflammation and because chronic P. aeruginosa lung infections are associated with a hyperinflammatory state, treatment with let-7b-5p and a beta-lactam antibiotic in nanoparticles or EVs may benefit patients with antibiotic-resistant P. aeruginosa infections.

Plasma DNA as a "liquid biopsy" incompletely complements tumor biopsy for identification of mutations in a case series of four patients with oligometastatic breast cancer.

PURPOSE: Circulating tumor DNA in plasma may present a minimally invasive opportunity to identify tumor-derived mutations to inform selection of targeted therapies for individual patients, particularly in cases of oligometastatic disease where biopsy of multiple tumors is impractical. To assess the utility of plasma DNA as a "liquid biopsy" for precision oncology, we tested whether sequencing of plasma DNA is a reliable surrogate for sequencing of tumor DNA to identify targetable genetic alterations. METHODS: Blood and biopsies of 1-3 tumors were obtained from 4 evaluable patients with advanced breast cancer. One patient provided samples from an additional 7 tumors post-mortem. DNA extracted from plasma, tumor tissues, and buffy coat of blood were used for probe-directed capture of all exons in 149 cancer-related genes and massively parallel sequencing. Somatic mutations in DNA from plasma and tumors were identified by comparison to buffy coat DNA. RESULTS: Sequencing of plasma DNA identified 27.94 ± 11.81% (mean ± SD) of mutations detected in a tumor(s) from the same patient; such mutations tended to be present at high allelic frequency. The majority of mutations found in plasma DNA were not found in tumor samples. Mutations were also found in plasma that matched clinically undetectable tumors found post-mortem. CONCLUSIONS: The incomplete overlap of genetic alteration profiles of plasma and tumors warrants caution in the sole reliance of plasma DNA to identify therapeutically targetable alterations in patients and indicates that analysis of plasma DNA complements, but does not replace, tumor DNA profiling. TRIAL REGISTRATION: Subjects were prospectively enrolled in trial NCT01836640 (registered April 22, 2013).

Associations in cell type-specific hydroxymethylation and transcriptional alterations of pediatric central nervous system tumors.

Although intratumoral heterogeneity has been established in pediatric central nervous system tumors, epigenomic alterations at the cell type level have largely remained unresolved. To identify cell type-specific alterations to cytosine modifications in pediatric central nervous system tumors, we utilize a multi-omic approach that integrated bulk DNA cytosine modification data (methylation and hydroxymethylation) with both bulk and single-cell RNA-sequencing data. We demonstrate a large reduction in the scope of significantly differentially modified cytosines in tumors when accounting for tumor cell type composition. In the progenitor-like cell types of tumors, we identify a preponderance differential Cytosine-phosphate-Guanine site hydroxymethylation rather than methylation. Genes with differential hydroxymethylation, like histone deacetylase 4 and insulin-like growth factor 1 receptor, are associated with cell type-specific changes in gene expression in tumors. Our results highlight the importance of epigenomic alterations in the progenitor-like cell types and its role in cell type-specific transcriptional regulation in pediatric central nervous system tumors.

Identifying tumor type and cell type-specific gene expression alterations in pediatric central nervous system tumors.

Central nervous system (CNS) tumors are the leading cause of pediatric cancer death, and these patients have an increased risk for developing secondary neoplasms. Due to the low prevalence of pediatric CNS tumors, major advances in targeted therapies have been lagging compared to other adult tumors. We collect single nuclei RNA-seq data from 84,700 nuclei of 35 pediatric CNS tumors and three non-tumoral pediatric brain tissues and characterize tumor heterogeneity and transcriptomic alterations. We distinguish cell subpopulations associated with specific tumor types including radial glial cells in ependymomas and oligodendrocyte precursor cells in astrocytomas. In tumors, we observe pathways important in neural stem cell-like populations, a cell type previously associated with therapy resistance. Lastly, we identify transcriptomic alterations among pediatric CNS tumor types compared to non-tumor tissues, while accounting for cell type effects on gene expression. Our results suggest potential tumor type and cell type-specific targets for pediatric CNS tumor treatment. Here we address current gaps in understanding single nuclei gene expression profiles of previously under-investigated tumor types and enhance current knowledge of gene expression profiles of single cells of various pediatric CNS tumors.

Hierarchal single-cell lineage tracing reveals differential fate commitment of CD8 T-cell clones in response to acute infection.

Generating balanced populations of CD8 effector and memory T cells is necessary for immediate and durable immunity to infections and cancer. Yet, a definitive understanding of CD8 differentiation remains unclear. We used CARLIN, a processive lineage recording mouse model with single-cell RNA-seq and TCR-seq to track endogenous antigen-specific CD8 T cells during acute viral infection. We identified a diverse repertoire of expanded T-cell clones represented by seven transcriptional states. TCR enrichment analysis revealed differential memory- or effector-fate biases within clonal populations. Shared Vb segments and amino acid motifs were found within biased categories despite high TCR diversity. Using single-cell CARLIN barcode-seq we tracked multi-generational clones and found that unlike unbiased or memory-biased clones, which stably retain their fate profiles, effector-biased clones could adopt memory- or effector-bias within subclones. Collectively, our study demonstrates that a heterogenous T-cell repertoire specific for a shared antigen is composed of clones with distinct TCR-intrinsic fate-biases.

Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis.

Graph-based deep learning has shown great promise in cancer histopathology image analysis by contextualizing complex morphology and structure across whole slide images to make high quality downstream outcome predictions (ex: prognostication). These methods rely on informative representations (i.e., embeddings) of image patches comprising larger slides, which are used as node attributes in slide graphs. Spatial omics data, including spatial transcriptomics, is a novel paradigm offering a wealth of detailed information. Pairing this data with corresponding histological imaging localized at 50-micron resolution, may facilitate the development of algorithms which better appreciate the morphological and molecular underpinnings of carcinogenesis. Here, we explore the utility of leveraging spatial transcriptomics data with a contrastive crossmodal pretraining mechanism to generate deep learning models that can extract molecular and histological information for graph-based learning tasks. Performance on cancer staging, lymph node metastasis prediction, survival prediction, and tissue clustering analyses indicate that the proposed methods bring improvement to graph based deep learning models for histopathological slides compared to leveraging histological information from existing schemes, demonstrating the promise of mining spatial omics data to enhance deep learning for pathology workflows.

Integrative Co-Registration of Elemental Imaging and Histopathology for Enhanced Spatial Multimodal Analysis of Tissue Sections through TRACE.

Summary: Elemental imaging provides detailed profiling of metal bioaccumulation, offering more precision than bulk analysis by targeting specific tissue areas. However, accurately identifying comparable tissue regions from elemental maps is challenging, requiring the integration of hematoxylin and eosin (H&E) slides for effective comparison. Facilitating the streamlined co-registration of Whole Slide Images (WSI) and elemental maps, TRACE enhances the analysis of tissue regions and elemental abundance in various pathological conditions. Through an interactive containerized web application, TRACE features real-time annotation editing, advanced statistical tools, and data export, supporting comprehensive spatial analysis. Notably, it allows for comparison of elemental abundances across annotated tissue structures and enables integration with other spatial data types through WSI co-registration. Availability and Implementation: Available on the following platforms- GitHub: jlevy44/trace_app , PyPI: trace_app , Docker: joshualevy44/trace_app , Singularity: joshualevy44/trace_app . Contact: joshua.levy@cshs.org. Supplementary information: Supplementary data are available.

Pharmacological induction of chromatin remodeling drives chemosensitization in triple-negative breast cancer.

Targeted therapies have improved outcomes for certain cancer subtypes, but cytotoxic chemotherapy remains a mainstay for triple-negative breast cancer (TNBC). The epithelial-to-mesenchymal transition (EMT) is a developmental program co-opted by cancer cells that promotes metastasis and chemoresistance. There are no therapeutic strategies specifically targeting mesenchymal-like cancer cells. We report that the US Food and Drug Administration (FDA)-approved chemotherapeutic eribulin induces ZEB1-SWI/SNF-directed chromatin remodeling to reverse EMT that curtails the metastatic propensity of TNBC preclinical models. Eribulin induces mesenchymal-to-epithelial transition (MET) in primary TNBC in patients, but conventional chemotherapy does not. In the treatment-naive setting, but not after acquired resistance to other agents, eribulin sensitizes TNBC cells to subsequent treatment with other chemotherapeutics. These findings provide an epigenetic mechanism of action of eribulin, supporting its use early in the disease process for MET induction to prevent metastatic progression and chemoresistance. These findings warrant prospective clinical evaluation of the chemosensitizing effects of eribulin in the treatment-naive setting.

Potential to Enhance Large Scale Molecular Assessments of Skin Photoaging through Virtual Inference of Spatial Transcriptomics from Routine Staining.

The advent of spatial transcriptomics technologies has heralded a renaissance in research to advance our understanding of the spatial cellular and transcriptional heterogeneity within tissues. Spatial transcriptomics allows investigation of the interplay between cells, molecular pathways, and the surrounding tissue architecture and can help elucidate developmental trajectories, disease pathogenesis, and various niches in the tumor microenvironment. Photoaging is the histological and molecular skin damage resulting from chronic/acute sun exposure and is a major risk factor for skin cancer. Spatial transcriptomics technologies hold promise for improving the reliability of evaluating photoaging and developing new therapeutics. Challenges to current methods include limited focus on dermal elastosis variations and reliance on self-reported measures, which can introduce subjectivity and inconsistency. Spatial transcriptomics offers an opportunity to assess photoaging objectively and reproducibly in studies of carcinogenesis and discern the effectiveness of therapies that intervene in photoaging and preventing cancer. Evaluation of distinct histological architectures using highly-multiplexed spatial technologies can identify specific cell lineages that have been understudied due to their location beyond the depth of UV penetration. However, the cost and interpatient variability using state-of-the-art assays such as the 10x Genomics Spatial Transcriptomics assays limits the scope and scale of large-scale molecular epidemiologic studies. Here, we investigate the inference of spatial transcriptomics information from routine hematoxylin and eosin-stained (H&E) tissue slides. We employed the Visium CytAssist spatial transcriptomics assay to analyze over 18,000 genes at a 50-micron resolution for four patients from a cohort of 261 skin specimens collected adjacent to surgical resection sites for basal cell and squamous cell keratinocyte tumors. The spatial transcriptomics data was co-registered with 40x resolution whole slide imaging (WSI) information. We developed machine learning models that achieved a macro-averaged median AUC and F1 score of 0.80 and 0.61 and Spearman coefficient of 0.60 in inferring transcriptomic profiles across the slides, and accurately captured biological pathways across various tissue architectures.

Tumor type and cell type-specific gene expression alterations in diverse pediatric central nervous system tumors identified using single nuclei RNA-seq.

Central nervous system (CNS) tumors are the leading cause of pediatric cancer death, and these patients have an increased risk for developing secondary neoplasms. Due to the low prevalence of pediatric CNS tumors, major advances in targeted therapies have been lagging compared to other adult tumors. We collected single nuclei RNA-seq data from 35 pediatric CNS tumors and three non-tumoral pediatric brain tissues (84,700 nuclei) and characterized tumor heterogeneity and transcriptomic alterations. We distinguished cell subpopulations associated with specific tumor types including radial glial cells in ependymomas and oligodendrocyte precursor cells in astrocytomas. In tumors, we observed pathways important in neural stem cell-like populations, a cell type previously associated with therapy resistance. Lastly, we identified transcriptomic alterations among pediatric CNS tumor types compared to non-tumor tissues, while accounting for cell type effects on gene expression. Our results suggest potential tumor type and cell type-specific targets for pediatric CNS tumor treatment. In this study, we address current gaps in understanding single nuclei gene expression profiles of previously uninvestigated tumor types and enhance current knowledge of gene expression profiles of single cells of various pediatric CNS tumors.

Hydroxymethylation alterations in progenitor-like cell types of pediatric central nervous system tumors are associated with cell type-specific transcriptional changes.

Although intratumoral heterogeneity has been established in pediatric central nervous system tumors, epigenomic alterations at the cell type level have largely remained unresolved. To identify cell type-specific alterations to cytosine modifications in pediatric central nervous system tumors we utilized a multi-omic approach that integrated bulk DNA cytosine modification data (methylation and hydroxymethylation) with both bulk and single-cell RNA-sequencing data. We demonstrate a large reduction in the scope of significantly differentially modified cytosines in tumors when accounting for tumor cell type composition. In the progenitor-like cell types of tumors, we identified a preponderance differential CpG hydroxymethylation rather than methylation. Genes with differential hydroxymethylation, like HDAC4 and IGF1R, were associated with cell type-specific changes in gene expression in tumors. Our results highlight the importance of epigenomic alterations in the progenitor-like cell types and its role in cell type-specific transcriptional regulation in pediatric CNS tumors.

Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis.

Graph-based deep learning has shown great promise in cancer histopathology image analysis by contextualizing complex morphology and structure across whole slide images to make high quality downstream outcome predictions (ex: prognostication). These methods rely on informative representations (i.e., embeddings) of image patches comprising larger slides, which are used as node attributes in slide graphs. Spatial omics data, including spatial transcriptomics, is a novel paradigm offering a wealth of detailed information. Pairing this data with corresponding histological imaging localized at 50-micron resolution, may facilitate the development of algorithms which better appreciate the morphological and molecular underpinnings of carcinogenesis. Here, we explore the utility of leveraging spatial transcriptomics data with a contrastive crossmodal pretraining mechanism to generate deep learning models that can extract molecular and histological information for graph-based learning tasks. Performance on cancer staging, lymph node metastasis prediction, survival prediction, and tissue clustering analyses indicate that the proposed methods bring improvement to graph based deep learning models for histopathological slides compared to leveraging histological information from existing schemes, demonstrating the promise of mining spatial omics data to enhance deep learning for pathology workflows.

Characterizing control of memory CD8 T cell differentiation by BTB-ZF transcription factor Zbtb20.

Members of the BTB-ZF transcription factor family regulate the immune system. Our laboratory identified that family member Zbtb20 contributes to the differentiation, recall responses, and metabolism of CD8 T cells. Here, we report a characterization of the transcriptional and epigenetic signatures controlled by Zbtb20 at single-cell resolution during the effector and memory phases of the CD8 T cell response. Without Zbtb20, transcriptional programs associated with memory CD8 T cell formation were up-regulated throughout the CD8 T response. A signature of open chromatin was associated with genes controlling T cell activation, consistent with the known impact on differentiation. In addition, memory CD8 T cells lacking Zbtb20 were characterized by open chromatin regions with overrepresentation of AP-1 transcription factor motifs and elevated RNA- and protein-level expressions of the corresponding AP-1 components. Finally, we describe motifs and genomic annotations from the DNA targets of Zbtb20 in CD8 T cells identified by cleavage under targets and release under nuclease (CUT&RUN). Together, these data establish the transcriptional and epigenetic networks contributing to the control of CD8 T cell responses by Zbtb20.

Alternating 17ß-Estradiol and Aromatase Inhibitor Therapies Is Efficacious in Postmenopausal Women with Advanced Endocrine-Resistant ER+ Breast Cancer.

PURPOSE: Strategies to implement estrogen therapy for advanced estrogen receptor-positive (ER+) breast cancer are underdeveloped. Preclinical data suggest that cycling treatment with 17ß-estradiol followed by estrogen deprivation can control tumor growth long-term. PATIENTS AND METHODS: Postmenopausal women with advanced ER+/HER2- breast cancer with recurrence or progression on ≥ 1 antiestrogen or aromatase inhibitor (AI)-based therapy were eligible. Patients received 17ß-estradiol (2 mg orally, three times a day) for 8 weeks followed by AI (physician's choice) for 16 weeks, alternating treatments on an 8-week/16-week schedule until disease progression. Patients then optionally received continuous single-agent treatment until a second instance of disease progression. Endpoints included 24-week clinical benefit and objective response per RECIST, and tumor genetic alterations. RESULTS: Of 19 evaluable patients, clinical benefit rate was 42.1% [95% confidence interval (CI), 23.1%-63.9%] and objective response rate (ORR) was 15.8% (95% CI, 5.7%-37.9%). One patient experienced a grade 3 adverse event related to 17ß-estradiol. Among patients who received continuous single-agent treatment until a second instance of disease progression, clinical benefit was observed in 5 of 12 (41.7%) cases. Tumor ER (ESR1) mutations were found by whole-exome profiling in 4 of 7 (57.1%) versus 2 of 9 (22.2%) patients who did versus did not experience clinical benefit from alternating 17ß-estradiol/AI therapy. The only two patients to experience objective responses to initial 17ß-estradiol had tumor ESR1 mutations. CONCLUSIONS: Alternating 17ß-estradiol/AI therapy may be a promising treatment for endocrine-refractory ER+ breast cancer, including following progression on CDK4/6 inhibitors or everolimus. Further study is warranted to determine whether the antitumor activity of 17ß-estradiol differs according to ESR1 mutation status.

The Overlooked Role of Specimen Preparation in Bolstering Deep Learning-Enhanced Spatial Transcriptomics Workflows.

The application of deep learning methods to spatial transcriptomics has shown promise in unraveling the complex relationships between gene expression patterns and tissue architecture as they pertain to various pathological conditions. Deep learning methods that can infer gene expression patterns directly from tissue histomorphology can expand the capability to discern spatial molecular markers within tissue slides. However, current methods utilizing these techniques are plagued by substantial variability in tissue preparation and characteristics, which can hinder the broader adoption of these tools. Furthermore, training deep learning models using spatial transcriptomics on small study cohorts remains a costly endeavor. Necessitating novel tissue preparation processes enhance assay reliability, resolution, and scalability. This study investigated the impact of an enhanced specimen processing workflow for facilitating a deep learning-based spatial transcriptomics assessment. The enhanced workflow leveraged the flexibility of the Visium CytAssist assay to permit automated H&E staining (e.g., Leica Bond) of tissue slides, whole-slide imaging at 40x-resolution, and multiplexing of tissue sections from multiple patients within individual capture areas for spatial transcriptomics profiling. Using a cohort of thirteen pT3 stage colorectal cancer (CRC) patients, we compared the efficacy of deep learning models trained on slide prepared using an enhanced workflow as compared to the traditional workflow which leverages manual tissue staining and standard imaging of tissue slides. Leveraging Inceptionv3 neural networks, we aimed to predict gene expression patterns across matched serial tissue sections, each stemming from a distinct workflow but aligned based on persistent histological structures. Findings indicate that the enhanced workflow considerably outperformed the traditional spatial transcriptomics workflow. Gene expression profiles predicted from enhanced tissue slides also yielded expression patterns more topologically consistent with the ground truth. This led to enhanced statistical precision in pinpointing biomarkers associated with distinct spatial structures. These insights can potentially elevate diagnostic and prognostic biomarker detection by broadening the range of spatial molecular markers linked to metastasis and recurrence. Future endeavors will further explore these findings to enrich our comprehension of various diseases and uncover molecular pathways with greater nuance. Combining deep learning with spatial transcriptomics provides a compelling avenue to enrich our understanding of tumor biology and improve clinical outcomes. For results of the highest fidelity, however, effective specimen processing is crucial, and fostering collaboration between histotechnicians, pathologists, and genomics specialists is essential to herald this new era in spatial transcriptomics-driven cancer research.

CCL5-producing migratory dendritic cells guide CCR5+ monocytes into the draining lymph nodes.

Dendritic cells (DCs) and monocytes capture, transport, and present antigen to cognate T cells in the draining lymph nodes (LNs) in a CCR7-dependent manner. Since only migratory DCs express this chemokine receptor, it is unclear how monocytes reach the LN. In steady-state and following inhalation of several PAMPs, scRNA-seq identified LN mononuclear phagocytes as monocytes, resident, or migratory type 1 and type 2 conventional (c)DCs, despite the downregulation of Xcr1, Clec9a, H2-Ab1, Sirpa, and Clec10a transcripts on migratory cDCs. Migratory cDCs, however, upregulated Ccr7, Ccl17, Ccl22, and Ccl5. Migratory monocytes expressed Ccr5, a high-affinity receptor for Ccl5. Using two tracking methods, we observed that both CD88hiCD26lomonocytes and CD88-CD26hi cDCs captured inhaled antigens in the lung and migrated to LNs. Antigen exposure in mixed-chimeric Ccl5-, Ccr2-, Ccr5-, Ccr7-, and Batf3-deficient mice demonstrated that while antigen-bearing DCs use CCR7 to reach the LN, monocytes use CCR5 to follow CCL5-secreting migratory cDCs into the LN, where they regulate DC-mediated immunity.