Microscopic examination of spatial transcriptome using Seq-Scope.

Spatial barcoding technologies have the potential to reveal histological details of transcriptomic profiles; however, they are currently limited by their low resolution. Here, we report Seq-Scope, a spatial barcoding technology with a resolution comparable to an optical microscope. Seq-Scope is based on a solid-phase amplification of randomly barcoded single-molecule oligonucleotides using an Illumina sequencing platform. The resulting clusters annotated with spatial coordinates are processed to expose RNA-capture moiety. These RNA-capturing barcoded clusters define the pixels of Seq-Scope that are ∼0.5-0.8 µm apart from each other. From tissue sections, Seq-Scope visualizes spatial transcriptome heterogeneity at multiple histological scales, including tissue zonation according to the portal-central (liver), crypt-surface (colon) and inflammation-fibrosis (injured liver) axes, cellular components including single-cell types and subtypes, and subcellular architectures of nucleus and cytoplasm. Seq-Scope is quick, straightforward, precise, and easy-to-implement and makes spatial single-cell analysis accessible to a wide group of biomedical researchers.

Holistic characterization of single-hepatocyte transcriptome responses to high-fat diet.

During nutritional overload and obesity, hepatocyte function is grossly altered, and a subset of hepatocytes begins to accumulate fat droplets, leading to nonalcoholic fatty liver disease (NAFLD). Recent single-cell studies revealed how nonparenchymal cells, such as macrophages, hepatic stellate cells, and endothelial cells, heterogeneously respond to NAFLD. However, it remains to be characterized how hepatocytes, the major constituents of the liver, respond to nutritional overload in NAFLD. Here, using droplet-based, single-cell RNA sequencing (Drop-seq), we characterized how the transcriptomic landscape of individual hepatocytes is altered in response to high-fat diet (HFD) and NAFLD. We showed that the entire hepatocyte population undergoes substantial transcriptome changes upon HFD, although the patterns of alteration were highly heterogeneous, with zonation-dependent and -independent effects. Periportal (zone 1) hepatocytes downregulated many zone 1-specific marker genes, whereas a small number of genes mediating gluconeogenesis were upregulated. Pericentral (zone 3) hepatocytes also downregulated many zone 3-specific genes; however, they upregulated several genes that promote HFD-induced fat droplet formation, consistent with findings that zone 3 hepatocytes accumulate more lipid droplets. Zone 3 hepatocytes also upregulated ketogenic pathways as an adaptive mechanism to HFD. Interestingly, many of the top HFD-induced genes, which encode proteins regulating lipid metabolism, were strongly co-expressed with each other in a subset of hepatocytes, producing a variegated pattern of spatial co-localization that is independent of metabolic zonation. In conclusion, our data set provides a useful resource for understanding hepatocellular alteration during NAFLD at single cell level.

Exemestane may be less detrimental than letrozole to bone health in women homozygous for the UGT2B17*2 gene deletion.

PURPOSE: UGT2B17 gene deletion (UGT2B17*2) has been reported to affect bone health as well as the pharmacokinetics of aromatase inhibitor (AI) drugs such as exemestane. The goal of this study was to assess associations between UGT2B17 gene deletion and bone health prior to and after 24 months of AI treatment in postmenopausal women with hormone receptor positive (HR+) breast cancer. METHODS: Bone health in women with HR+ breast cancer enrolled on the prospective randomized Exemestane and Letrozole Pharmacogenetics (ELPh) trial was determined by measuring bone turnover markers (BTM) and bone mineral density (BMD) pre-treatment and after 3 BTM and 24 BMD months of treatment with either the steroidal AI exemestane or the nonsteroidal AI letrozole. DNA samples were genotyped for UGT2B17*2. RESULTS: Of the 455 subjects included in the analyses, 244 (53.6%) carried at least one copy of UGT2B17*2. UGT2B17*2 was associated with lower pre-treatment BMD at the hip (P = 0.01) and spine (P = 0.0076). Letrozole treatment was associated with a greater decrease in BMD of the hip (P = 0.03) and spine (P = 0.03) than exemestane. UGT2B17 genotype was not associated with changes in BMD from 24 months of AI treatment, though in UGT2B17*2 homozygous patients, there was a trend toward greater decreases in BMD of the spine from treatment with letrozole compared with exemestane (P = 0.05). CONCLUSION: UGT2B17*2 may be associated with lower baseline BMD in women with HR+ breast cancer. Exemestane is less detrimental to bone health than letrozole in postmenopausal women treated with AI, and this effect may be confined to patients carrying UGT2B17*2, though this finding requires independent validation.

Attempted replication of SNPs in RANKL and OPG with musculoskeletal adverse events during aromatase inhibitor treatment for breast cancer.

Aromatase inhibitor (AI) therapy is highly efficacious in the treatment of estrogen receptor-positive breast cancer; however, in a subset of patients AI use is discontinued due to drug-induced musculoskeletal adverse events (MS-AE). Several studies have investigated the role of germline single nucleotide polymorphisms (SNPs) on patients' risk of MS-AEs; however, no associations have yet to be validated for translation into clinical practice. This study attempted to replicate SNPs in RANKL ( rs7984870 ) and OPG ( rs2073618 ) on the risk of AI-induced MS-AEs and screen for secondary associations with MS-AE-related treatment discontinuation and serum and urine markers of bone health. Previously reported associations were not replicated with our primary hypothesis, change in MS-AE from baseline to 3 mo; however, patients homozygous for the G allele of rs7984870 in RANKL had lower risk of MS-AE-associated treatment discontinuation in analyses of secondary phenotypes without statistical correction.

Seq-Scope Protocol: Repurposing Illumina Sequencing Flow Cells for High-Resolution Spatial Transcriptomics.

Spatial transcriptomics (ST) technologies represent a significant advance in gene expression studies, aiming to profile the entire transcriptome from a single histological slide. These techniques are designed to overcome the constraints faced by traditional methods such as immunostaining and RNA in situ hybridization, which are capable of analyzing only a few target genes simultaneously. However, the application of ST in histopathological analysis is also limited by several factors, including low resolution, a limited range of genes, scalability issues, high cost, and the need for sophisticated equipment and complex methodologies. Seq-Scope-a recently developed novel technology-repurposes the Illumina sequencing platform for high-resolution, high-content spatial transcriptome analysis, thereby overcoming these limitations. Here we provide a detailed step-by-step protocol to implement Seq-Scope with an Illumina NovaSeq 6000 sequencing flow cell that allows for the profiling of multiple tissue sections in an area of 7 mm × 7 mm or larger. In addition to detailing how to prepare a frozen tissue section for both histological imaging and sequencing library preparation, we provide comprehensive instructions and a streamlined computational pipeline to integrate histological and transcriptomic data for high-resolution spatial analysis. This includes the use of conventional software tools for single cell and spatial analysis, as well as our recently developed segmentation-free method for analyzing spatial data at submicrometer resolution. Given its adaptability across various biological tissues, Seq-Scope establishes itself as an invaluable tool for researchers in molecular biology and histology.

Seq-Scope: repurposing Illumina sequencing flow cells for high-resolution spatial transcriptomics.

Spatial transcriptomics technologies aim to advance gene expression studies by profiling the entire transcriptome with intact spatial information from a single histological slide. However, the application of spatial transcriptomics is limited by low resolution, limited transcript coverage, complex procedures, poor scalability and high costs of initial setup and/or individual experiments. Seq-Scope repurposes the Illumina sequencing platform for high-resolution, high-content spatial transcriptome analysis, overcoming these limitations. It offers submicrometer resolution, high capture efficiency, rapid turnaround time and precise annotation of histopathology at a much lower cost than commercial alternatives. This protocol details the implementation of Seq-Scope with an Illumina NovaSeq 6000 sequencing flow cell, allowing the profiling of multiple tissue sections in an area of 7 mm × 7 mm or larger. We describe the preparation of a fresh-frozen tissue section for both histological imaging and sequencing library preparation and provide a streamlined computational pipeline with comprehensive instructions to integrate histological and transcriptomic data for high-resolution spatial analysis. This includes the use of conventional software tools for single-cell and spatial analysis, as well as our recently developed segmentation-free method for analyzing spatial data at submicrometer resolution. Aside from array production and sequencing, which can be done in batches, tissue processing, library preparation and running the computational pipeline can be completed within 3 days by researchers with experience in molecular biology, histology and basic Unix skills. Given its adaptability across various biological tissues, Seq-Scope establishes itself as an invaluable tool for researchers in molecular biology and histology.

SiftCell: A robust framework to detect and isolate cell-containing droplets from single-cell RNA sequence reads.

Single-cell RNA sequencing (scRNA-seq) massively profiles transcriptomes of individual cells encapsulated in barcoded droplets in parallel. However, in real-world scRNA-seq data, many barcoded droplets do not contain cells, but instead, they capture a fraction of ambient RNAs released from damaged or lysed cells. A typical first step to analyze scRNA-seq data is to filter out cell-free droplets and isolate cell-containing droplets, but distinguishing them is often challenging; incorrect filtering may mislead the downstream analysis substantially. We propose SiftCell, a suite of software tools to identify and visualize cell-containing and cell-free droplets in manifold space via randomization (SiftCell-Shuffle) to classify between the two types of droplets (SiftCell-Boost) and to quantify the contribution of ambient RNAs for each droplet (SiftCell-Mix). By applying our method to datasets obtained by various single-cell platforms, we show that SiftCell provides a streamlined way to perform upstream quality control of scRNA-seq, which is more comprehensive and accurate than existing methods.

Single cell and spatial sequencing define processes by which keratinocytes and fibroblasts amplify inflammatory responses in psoriasis.

The immunopathogenesis of psoriasis, a common chronic inflammatory disease of the skin, is incompletely understood. Here we demonstrate, using a combination of single cell and spatial RNA sequencing, IL-36 dependent amplification of IL-17A and TNF inflammatory responses in the absence of neutrophil proteases, which primarily occur within the supraspinous layer of the psoriatic epidermis. We further show that a subset of SFRP2+ fibroblasts in psoriasis contribute to amplification of the immune network through transition to a pro-inflammatory state. The SFRP2+ fibroblast communication network involves production of CCL13, CCL19 and CXCL12, connected by ligand-receptor interactions to other spatially proximate cell types: CCR2+ myeloid cells, CCR7+ LAMP3+ dendritic cells, and CXCR4 expressed on both CD8+ Tc17 cells and keratinocytes, respectively. The SFRP2+ fibroblasts also express cathepsin S, further amplifying inflammatory responses by activating IL-36G in keratinocytes. These data provide an in-depth view of psoriasis pathogenesis, which expands our understanding of the critical cellular participants to include inflammatory fibroblasts and their cellular interactions.

STtools: A Comprehensive Software Pipeline for Ultra-high Resolution Spatial Transcriptomics Data.

Motivation: While there are many software pipelines for analyzing spatial transcriptomics data, few can process ultra high-resolution datasets generated by emerging technologies. There is a clear need for new software tools that can handle sub-micrometer resolution spatial transcriptomics data with computational scalability without compromising its resolution. Results: We developed STtools, a software pipeline that provides a versatile framework to handle spatial transcriptomics datasets with various resolutions, such as the ones produced by Seq-Scope (<1µm), Slide-seq (10µm) and VISIUM (100µm). It automatically processes raw FASTQ files and runs downstream analyses at several folds higher resolution than existing methods. It also generates various visualizations including transcriptome density, cell type mapping, marker gene highlighting, and subcellular architectures. Availability: STtools is publically available for download at https://github.com/seqscope/STtools.

TREM2 macrophages induced by human lipids drive inflammation in acne lesions.

Acne affects 1 in 10 people globally, often resulting in disfigurement. The disease involves excess production of lipids, particularly squalene, increased growth of Cutibacterium acnes, and a host inflammatory response with foamy macrophages. By combining single-cell and spatial RNA sequencing as well as ultrahigh-resolution Seq-Scope analyses of early acne lesions on back skin, we identified TREM2 macrophages expressing lipid metabolism and proinflammatory gene programs in proximity to hair follicle epithelium expressing squalene epoxidase. We established that the addition of squalene induced differentiation of TREM2 macrophages in vitro, which were unable to kill C. acnes. The addition of squalene to macrophages inhibited induction of oxidative enzymes and scavenged oxygen free radicals, providing an explanation for the efficacy of topical benzoyl peroxide in the clinical treatment of acne. The present work has elucidated the mechanisms by which TREM2 macrophages and unsaturated lipids, similar to their involvement in atherosclerosis, may contribute to the pathogenesis of acne.

Comparison of Nine Tools for Screening Drug-Drug Interactions of Oral Oncolytics.

PURPOSE: Patients with cancer are an especially vulnerable population to potential drug-drug interactions (DDIs). This makes it important to adequately screen them for DDIs. The objective of this study was to compare the abilities of nine DDI screening tools to detect clinically relevant interactions with oral oncolytics. METHODS: Subscription-based tools (ie, PEPID, Micromedex, Lexicomp, Facts & Comparisons) and free tools (ie, Epocrates Free, Medscape, Drugs.com, RxList, WebMD) were compared for their abilities to detect clinically relevant DDIs for 145 drug pairs including an oral oncology agent. Clinical relevance was determined by a pharmacist using Stockley's Drug Interactions. Descriptive statistics were calculated for each tool, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), and then compared grouped by free or subscription-based tools for the secondary analysis and analyzed via generalized estimating equations. RESULTS: For individual metrics, PPV had overall higher values (0.88 to 0.97) relative to the low values included for sensitivity (0.65 to 0.96), specificity (0.53 to 0.93) and NPV (0.38 to 0.83). The top-performing subscription and free tools, Lexicomp and Drugs.com, had no statistically significant differences in performance. Overall, subscription tools had a significantly higher sensitivity (0.85 ± 0.017 v 0.78 ± 0.017; P = .0082) and NPV (0.57 ± 0.039 v 0.48 ± 0.032; P = .031) than free tools. No differences were observed between the specificity and PPV. CONCLUSION: Due to the low performance of some tools for sensitivity, specificity, and NPV, individual performance should be examined and prioritized on the basis of the intended use when selecting a DDI tool. If a strong-performing subscription-based tool is unavailable, a strong-performing free option, like Drugs.com, is available.