10X Genomics (Sweden)

Abstract Multi-analyte single cell technologies have potential to greatly accelerate antibody discovery by identifying antigen-specific B cells. Barcode Enabled Antigen Mapping (BEAM) provides a high-throughput, multimodal analysis of antigen-bound B cells (BEAM-Ab) using 10x Genomics Chromium Single Cell Immune Profiling v2 Solution and novel computational approaches to discover B-cell receptor (BCR) sequences for further functional characterization. To demonstrate the antigen sensitivity of BEAM-Ab, we spiked in 5% Hen Egg Lysozyme (HEL) and 5% gp120 transgenic B cells in 90% wild type non-transgenic splenocytes. The cells were screened using a panel of barcoded antigens and then CD19+PE+ (antigen+) B cells were isolated using flow cytometry. Single cell gene expression, BCR, and antigen barcode analysis indicated clonotype specificity to the respective antigens in the sorted cells. Furthermore, we demonstrated BEAM-Ab specificity by analyzing a convalescent COVID patient sample against an antigen panel containing five different COVID antigens and a negative control. Analysis of this COVID sample suggests the clonal expansion of B cells recognizing the ectodomain of the wild type SARS-CoV2 spike protein, receptor binding domain of spike protein, and the ectodomain of the D614G mutant spike protein. We did not observe any clonal expansion to Omicron-specific antigen or the non-specific negative control in our dataset. Our data demonstrates that BEAM-Ab is an effective antibody discovery tool. BEAM-Ab unlocks the ability to rapidly screen large numbers of samples with an accurate single cell resolution and antigen specificity, with the entire workflow generating the candidate sequences just one week after sample processing.

Abstract The tumor microenvironment is composed of highly heterogeneous niches, often with varying degrees of immune infiltration. The spatial distribution of immune cells with respect to malignant cells can directly impact patient prognosis and overall survival outcomes. The Visium CytAssist Spatial Gene Expression assay uses a whole transcriptome probe-based approach, termed RTL, to detect and quantify mRNA expression with spatial context. Although examination of the tumor microenvironment with an RTL-based spatial assay can provide significant transcriptomic information concerning regions of interest, immune cells frequently have extremely low mRNA expression levels and can be difficult to detect. The use of antibody-conjugated probes specific to immune cell epitopes, which are highly expressed, can enhance data recovered from these tumor samples, enabling spatially accurate detection of immune populations. The Visium CytAssist Spatial Proteogenomic Solution enables identification of immune-specific epitopes via antibody-conjugated probes from the same tissue slide used for transcriptomic analysis. Using the CytAssist workflow, we showcase the ability to comprehensively resolve immune cells associated with multiple immune and tumor tissues, including an array of human breast cancer punches. Spatial expression patterns of immune markers map back to distinct morphological features within the samples, allowing identification of differentially-expressed genes associated with those areas. Overall, these data highlight the value of Visium CytAssist Spatial Proteogenomic Solution in immuno-oncology studies, through the integration of spatially resolved transcriptomic and immune cell marker data.

Resident T lymphocytes (TRM) protect tissues during pathogen reexposure. Although TRM phenotype and restricted migratory pattern are established, we have a limited understanding of their response kinetics, stability, and turnover during reinfections. Such characterizations have been restricted by the absence of in vivo fate-mapping systems. We generated two mouse models, one to stably mark CD103+ T cells (a marker of TRM cells) and the other to specifically deplete CD103- T cells. Using these models, we observed that intestinal CD103+ T cells became activated during viral or bacterial reinfection, remained organ-confined, and retained their original phenotype but failed to reexpand. Instead, the population was largely rejuvenated by CD103+ T cells formed de novo during reinfections. This pattern remained unchanged upon deletion of antigen-specific circulating T cells, indicating that the lack of expansion was not due to competition with circulating subsets. Thus, although intestinal CD103+ resident T cells survived long term without antigen, they lacked the ability of classical memory T cells to reexpand. This indicated that CD103+ T cell populations could not autonomously maintain themselves. Instead, their numbers were sustained during reinfection via de novo formation from CD103- precursors. Moreover, in contrast to CD103- cells, which require antigen plus inflammation for their activation, CD103+ TRM became fully activated follwing exposure to inflammation alone. Together, our data indicate that primary CD103+ resident memory T cells lack secondary expansion potential and require CD103- precursors for their long-term maintenance.

Abstract We have optimized an experimental and computational pipeline to adapt spatial transcriptomics (ST) approaches based upon the Visium (10x Genomics) technology to infer cellular composition and intercellular interactions of FFPE clinical specimens. We apply this technology to deliver an approach to examine pancreatic intraepithelial neoplasia (PanIN) to identify intrinsic and extrinsic mechanisms that are associated with the progression of these pre-malignant lesions to invasive carcinoma. Currently, most pancreatic cancers are diagnosed at an advanced stage that reflects in dismal survival rates and a better understanding of PanINs biology will provide valuable insights for early therapeutic interventions. Thus, we used PanINs as our model system to implement the FFPE ST workflow. Our workflow for FFPE ST analysis facilitates sectioning of small regions (5mm in diameter) from a paraffin block that are stained and imaged with H&E and concurrently measured for genome-wide transcriptional profiling. Subsequently, the image is used for automated cell annotation using an algorithm, CODA, trained to identify normal and neoplastic pancreatic cell types. CODA identified the normal pancreatic histological regions (ducts, acini, islets of Langerhans, stroma), as well as the neoplastic cells. This automated analysis enables isolation of specific spots for differential expression analysis to pinpoint the transcriptional changes that occur within neoplastic cells along ducts in PanIn and their changes between high-grade and low-grade lesions. The spatial gene expression analysis identified clusters that mapped to the cell types annotated by CODA and the marker genes of each cluster matched known markers for the correspondent cell type. Although PanINs are very small in size (< 1mm), we found specific clusters accurately mapped to these lesions in each sample. Overall, the spatial sequencing data presented enough depth and complexity to allow differential expression and pathway analysis. We observed a significant number of deregulated genes in PanINs compared to normal ducts. Some deregulated genes are known PanIN markers, but potential new markers were also identified. Moreover, the integration of CODA with gene expression changes enables us to verify that unique stromal regions annotated with CODA and associated with PanIns are in fact heterogeneous and formed by distinct cell subtypes. Altogether, our workflow combining automated cell annotation with STA from the same section provides a methodology to precisely examine the sample architecture while measuring heterogeneity at the transcriptional level. This combined approach can be applied to different FFPE tumor types to leverage the use of large bioarchives of samples not previously accessible to genome-wide spatial methods. Citation Format: Alexander T. Bell, Kohei Fujikura, Jacob Stern, Rena Chan, James Chell, Stephen Williams, Ashley Kiemen, Elizabeth M. Jaffee, Denis Wirtz, Laura D. Wood, Elana J. Fertig, Luciane T. Kagohara. Spatial transcriptomics for FFPE characterizes the molecular and cellular architecture of malignant changes in pancreatic pre-malignant lesions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 637.

Abstract Progress in our understanding of immunology and cancer immunotherapies requires a comprehensive view of immune cell behavior and the interactions of these cells with their environment. Recent technological innovations have facilitated the combination of cell-surface protein, transcriptome, immune repertoire, and antigen specificity measurements from the same single cells, providing thorough and high-throughput lymphocyte characterization. Using the 10x Genomics Single Cell Immune Profiling Solution with Feature Barcoding technology along with oligo-conjugated antibodies and peptide-MHC (pMHC) Dextramers®, we performed multi-omic characterization of PBMCs from cytomegalovirus (CMV) seronegative and seropositive patients. Next generation sequencing libraries were made following the 10x Genomics workflows, where transcriptome and immune repertoire libraries are generated alongside libraries from DNA barcodes conjugated to antibodies or pMHC. Full length, paired TCRα/β sequences with specificity to known CMV antigens were identified in the seropositive donor, but not in the seronegative donor. Interestingly, a large Epstein Barr Virus (EBV) pMHC specific T cell expansion was identified in the CMV seronegative donor, suggesting an active EBV response. Moreover, the combination of transcriptomic and cell surface protein information resulted in an increase in resolution of cell type identification. This multi-omic workflow allowed the identification of enriched amino acid motifs within the TCR sequences that contained novel and known CDR3 amino acid sequences specific to CMV. These technological advancements provide new biological insights that are critical for progress in the field.

Using a search engine called Motifer, we searched the public database of the human genome for genes matching a consensus pattern of cysteine residues derived from members of the transforming growth factor-beta (TGF-beta) superfamily. We identified two genes (named MDF451 and MDF628) that display sequence similarity to members of the TGF-beta superfamily in the arrangement of six conserved cysteine residues. Phylogenetic analyses revealed that MDF451 and MDF628 constitute a distinct subgroup within the TGF-beta superfamily, distantly related to the GDNF subfamily of ligands. Both genes could be identified in several primate species in addition to human, including chimpanzee, gorilla, guereza, and green and gray monkey, but not in rodents or other non-primate mammals, and appear not to be present in the genomes of mouse, rat or zebrafish. RNAs for MDF451 and MDF628 were expressed at low levels within distinct regions of the human central nervous system, including adult cerebellum, adult spinal cord and fetal brain. Despite expression at the RNA level, both genes presented a transcribed upstream stop codon that would prevent translation of the TGF-beta-like reading frame. The coding potential of alternative reading frames was not immediately apparent. The two genes may represent TGF-beta-like pseudogenes that have recently appeared in evolution in a common ancestor of the primate lineage by duplication from a GDNF/TGF-beta-like ancestral gene.