Emerging spatial profiling technology has enabled high-plex molecular profiling in biological tissues, preserving the spatial and morphological context of gene expression. Here we describe expanding the chemistry for the Digital Spatial Profiling platform to quantify whole transcriptomes in human and mouse tissues using a wide range of spatial profiling strategies and sample types. We designed multiplexed in situ hybridization probe pools targeting the protein-coding genes in the human and mouse transcriptomes, hereafter referred to as the human or mouse Whole Transcriptome Atlas (WTA). We validated the human and mouse WTA using cell lines to demonstrate concordance with orthogonal gene expression profiling methods in profiled region sizes ranging from ~10-500 cells. By benchmarking against bulk RNAseq and fluorescence in situ hybridization, we demonstrate robust transcript detection possible down to ~100 transcripts per region. To assess the performance of WTA across tissue and sample types, we applied WTA to biological questions in cancer, molecular pathology, and developmental biology. We show that spatial profiling with WTA can detect expected spatial gene expression differences between tumor and tumor microenvironment, identify spatial disease-specific heterogeneity in gene expression in histological structures of the human kidney, and comprehensively map transcriptional programs in anatomical substructures of nine organs in the developing mouse embryo. Digital Spatial Profiling technology with the WTA assays provides a flexible method for spatial whole transcriptome profiling applicable to diverse tissue types and biological contexts.

610 Background: Pancreatic ductal adenocarcinoma (PDAC) is highly lethal and resistance to chemotherapy and radiotherapy is a major obstacle to improving clinical outcomes. Hence, there is an urgent need to elucidate the gene expression programs, spatial context, and interactions among different cell types in residual disease after neoadjuvant treatment. Methods: We optimized and applied single-nucleus RNA-seq (snRNA-seq) to 43 frozen primary PDAC tumors. Eighteen were treatment-naïve, 14 received FOLFIRINOX followed by radiotherapy with 5-FU or capecitabine (CRT), and 5 were subjected to CRT combined with losartan on protocol (CRTL). We performed unsupervised clustering of single nucleus profiles and then annotated and quantified cell subsets. Malignant and fibroblast gene expression programs were identified by consensus non-negative matrix factorization (cNMF). We mapped our cell type signatures and expression programs onto the tumor architecture using whole-transcriptome digital spatial profiling (DSP) to uncover distinct multicellular spatial neighborhoods and intercellular interactions that compose PDAC and are remodeled by neoadjuvant treatment. Results: Consistent with treatment effect, the proportion of malignant cells was significantly lower in tumors treated with neoadjuvant therapy. Within the immune compartment, CRTL was associated with a higher fraction of CD8+ T cells and Tregs compared to untreated and CRT tumors. Differential expression analysis of CD8+ T cells revealed greater effector function (e.g., IL2, CCL4, CCL5) and reduced quiescence/dysfunction markers (e.g., TIGIT, TCF7, KLF2, LEF1) associated with CRTL. We discovered expression programs across malignant and fibroblast profiles that formed a refined molecular taxonomy, including a novel neuronal-like malignant program enriched in the neoadjuvant groups and associated with the worst prognosis in independent cohorts. Ex vivo treatment of organoids derived from an untreated PDAC with FOLFIRINOX chemotherapy and radiotherapy recapitulated enrichment of the neuronal-like program. Whole-transcriptome DSP revealed three distinct multicellular neighborhoods: classical, squamoid-basaloid, and treatment-enriched. The observed enrichment in post-treatment residual disease of multiple spatially-defined receptor-ligand interactions and a neighborhood featuring colocalization of the neuronal-like malignant program, neurotropic CAF program, and CD8+ T cells may open new opportunities for therapeutic targeting in PDAC. Conclusions: Our work provides a high-resolution molecular framework for understanding the inter- and intra-tumoral heterogeneity of pancreatic cancer, spatial organization into discrete multicellular communities, and treatment-associated reprogramming as a blueprint for exploring novel therapeutic strategies tailored to residual disease.

Pancreatic ductal adenocarcinoma (PDAC) is projected to be the second leading cause of cancer mortality in the United States by 2030. Given that resistance to cytotoxic therapy is pervasive, there is a critical need to elucidate salient gene expression programs and spatial relationships among malignant and stromal cells in the tumor microenvironment (TME), particularly in residual disease. We developed and applied a single-nucleus RNA-seq (snRNA-seq) technique to 43 banked frozen primary PDAC specimens that either received neoadjuvant therapy (n=25) or were treatment-naïve (n=18). We discovered expression programs across malignant cell and fibroblast profiles that formed the basis for a refined molecular taxonomy, including a novel neural-like progenitor (NRP) malignant program enriched with neoadjuvant treatment in tumors and organoids, and associated with the worst prognosis in bulk profiles from independent cohorts. To elucidate how neoadjuvant treatment and cancer cell- and fibroblast-intrinsic programs modulate the composition of multicellular neighborhoods, we performed spatial profiling with the GeoMx[1] platform (NanoString) on 21 formalin-fixed paraffin-embedded sections using the human whole transcriptome atlas (WTA). Each tumor showed intra-tumoral heterogeneity in tissue architecture and regions of interest (ROIs) with diverse patterns of neoplastic cells, cancer-associated fibroblasts (CAFs), and immune cells were selected for profiling. We deconvolved the WTA data with our snRNA-seq cell type signatures and mapped expression programs onto the tumor architecture to reveal three distinct multicellular neighborhoods, which we annotated as classical, squamoid-basaloid, and treatment-enriched. The observed enrichment in post-treatment residual disease of multiple spatially-defined receptor-ligand interactions and a neighborhood featuring the NRP program, neurotropic CAF program, and CD8+ T cells may open new therapeutic opportunities. Next, we mapped malignant/CAF programs and immune cell subsets at single-cell spatial resolution by performing spatial molecular imaging (SMI[2]; NanoString CosMx) using a panel of 960 RNA targets on a subset of seven tumors (2 untreated, 5 treated) and captured over 200,000 cells with an average of more than 450 transcripts detected per cell. Correlating ROIs from whole-transcriptome DSP to matched fields of view in kiloplex SMI enabled further dissection of PDAC architecture and treatment-associated remodeling of cell type distributions and receptor-ligand interactions. Ongoing functional studies have begun to elucidate the key regulatory elements underlying the distinct treatment-associated NRP malignant program and its interactions with the TME. Overall, the complementary combination of snRNA-seq, whole-transcriptome DSP, and kiloplex SMI provides a high-resolution molecular framework that can be harnessed to augment precision oncology efforts in pancreatic cancer. [1] GeoMx DSP is for Research Use Only and not for use in diagnostic procedures. [2] CosMx SMI is for Research Use Only and not for use in diagnostic procedures. Citation Format: William L. Hwang, Karthik A. Jagadeesh, Jimmy A. Guo, Hannah I. Hoffman, Carina Shiau, Jennifer Su, Payman Yadollahpour, Jason W. Reeves, Youngmi Kim, Sean Kim, Mark Gregory, Prajan Divakar, Eric Miller, Michael Rhodes, Sarah Warren, Erroll Rueckert, Kit Fuhrman, Daniel R. Zollinger, Robin Fropf, Joseph M. Beechem, Arnav Mehta, Toni Delorey, Cristin McCabe, Jaimie L. Barth, Piotr Zelga, Cristina R. Ferrone, Motaz Qadan, Keith D. Lillemoe, Rakesh K. Jain, Jennifer Y. Wo, Theodore S. Hong, Ramnik Xavier, Orit Rozenblatt-Rosen, Andrew J. Aguirre, Carlos Fernandez-Del Castillo, Andrew S. Liss, Mari Mino-Kenudson, David T. Ting, Tyler Jacks, Aviv Regev. Multicellular spatial community featuring a novel neuronal-like malignant phenotype is enriched in pancreatic cancer after neoadjuvant chemotherapy and radiotherapy [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 SY12-04.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal and treatment-refractory cancer. Molecular stratification in pancreatic cancer remains rudimentary and does not yet inform clinical management or therapeutic development. Here, we construct a high-resolution molecular landscape of the cellular subtypes and spatial communities that compose PDAC using single-nucleus RNA sequencing and whole-transcriptome digital spatial profiling (DSP) of 43 primary PDAC tumor specimens that either received neoadjuvant therapy or were treatment naive. We uncovered recurrent expression programs across malignant cells and fibroblasts, including a newly identified neural-like progenitor malignant cell program that was enriched after chemotherapy and radiotherapy and associated with poor prognosis in independent cohorts. Integrating spatial and cellular profiles revealed three multicellular communities with distinct contributions from malignant, fibroblast and immune subtypes: classical, squamoid-basaloid and treatment enriched. Our refined molecular and cellular taxonomy can provide a framework for stratification in clinical trials and serve as a roadmap for therapeutic targeting of specific cellular phenotypes and multicellular interactions.

Abstract A molecular classification of pancreatic ductal adenocarcinoma (PDAC) that informs clinical management remains elusive. Previously identified bulk expression subtypes in the untreated setting were influenced by contaminating stroma whereas single cell RNA-seq (scRNA-seq) of fresh tumors under-represented key cell types. Two consensus subtypes have arisen from these prior efforts: (1) classical-like, and (2) basal-like. Basal-like tumors were associated with worse survival in the metastatic setting but attempts to refine this binary classification have failed to further stratify patient survival. Here, we developed a robust single-nucleus RNA-seq (snRNA-seq) technique for banked frozen PDAC specimens and studied a cohort of untreated resected primary tumors (n ~ 20). Gene expression programs learned across malignant cell and cancer-associated fibroblast (CAF) profiles uncovered a clinically-relevant molecular taxonomy with improved prognostic stratification compared to prior classifications. Digital spatial profiling revealed an association between malignant cells expressing basal-like programs and greater immune infiltration with relatively fewer macrophages, whereas those exhibiting classical-like programs were linked to inflammatory CAFs and macrophage-predominant microniches. Recent clinical trials have supported the increasing adoption of neoadjuvant therapy to aggressively address the risk of micro-metastatic spread and to circumvent concerns of treatment tolerance in the postoperative setting. There is an urgent need to understand how preoperative treatment impacts residual tumor cells and their interactions with other cell types in the tumor microenvironment to identify additional therapeutic vulnerabilities that can be exploited. Towards this end, we performed snRNA-seq on an unmatched cohort of neoadjuvant-treated resected primary tumors (n ~ 25) with most cases involving FOLFIRINOX chemotherapy followed by chemoradiation. Remarkably, the quality of single-nucleus mRNA profiles was comparable between heavily pre-treated and untreated specimens. We identified differentially expressed genes between treated and untreated samples to infer cell-type specific reprogramming in the residual tumor. This analysis revealed that in the neoadjuvant treatment context, there was lower expression of classical-like phenotypes in malignant cells in favor of basal-like phenotypes associated with TNF-NFkB and interferon signaling as well as the presence of novel acinar and neuroendocrine classical-like states. Our refined molecular taxonomy and spatial resolution may help advance precision oncology in PDAC through informative stratification in clinical trials and insights into compartment-specific therapies. Citation Format: William L. Hwang, Karthik A. Jagadeesh, Jimmy A. Guo, Hannah I. Hoffman, Payman Yadollahpour, Jason Reeves, Eugene Drokhlyansky, Nicholas Van Wittenberghe, Samouil Farhi, Denis Schapiro, George Eng, Jason M. Schenkel, William A. Freed-Pastor, Orr Ashenberg, Clifton Rodrigues, Domenic Abbondanza, Toni Delorey, Devan Phillips, Jorge Roldan, Debora Ciprani, Marina Kern, Jaimie L. Barth, Daniel R. Zollinger, Kit Fuhrman, Robin Fropf, Joseph Beechem, Colin Weekes, Cristina R. Ferrone, Jennifer Y. Wo, Theodore S. Hong, Orit Rozenblatt-Rosen, Andrew J. Aguirre, Mari Mino-Kenudson, Carlos Fernandez-del- Castillo, Andrew S. Liss, David T. Ting, Tyler Jacks, Aviv Regev. Multi-compartment reprogramming and spatially-resolved interactions in frozen pancreatic cancer with and without neoadjuvant chemotherapy and radiotherapy at single-cell resolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 94.

The arrangement (syntax) of transcription factor (TF) binding motifs is an important part of the cis-regulatory code, yet remains elusive. We introduce a deep learning model, BPNet, that uses DNA sequence to predict base-resolution chromatin immunoprecipitation (ChIP)-nexus binding profiles of pluripotency TFs. We develop interpretation tools to learn predictive motif representations and identify soft syntax rules for cooperative TF binding interactions. Strikingly, Nanog preferentially binds with helical periodicity, and TFs often cooperate in a directional manner, which we validate using clustered regularly interspaced short palindromic repeat (CRISPR)-induced point mutations. Our model represents a powerful general approach to uncover the motifs and syntax of cis-regulatory sequences in genomics data.

COVID-19, which is caused by SARS-CoV-2, can result in acute respiratory distress syndrome and multiple organ failure1–4, but little is known about its pathophysiology. Here we generated single-cell atlases of 24 lung, 16 kidney, 16 liver and 19 heart autopsy tissue samples and spatial atlases of 14 lung samples from donors who died of COVID-19. Integrated computational analysis uncovered substantial remodelling in the lung epithelial, immune and stromal compartments, with evidence of multiple paths of failed tissue regeneration, including defective alveolar type 2 differentiation and expansion of fibroblasts and putative TP63+ intrapulmonary basal-like progenitor cells. Viral RNAs were enriched in mononuclear phagocytic and endothelial lung cells, which induced specific host programs. Spatial analysis in lung distinguished inflammatory host responses in lung regions with and without viral RNA. Analysis of the other tissue atlases showed transcriptional alterations in multiple cell types in heart tissue from donors with COVID-19, and mapped cell types and genes implicated with disease severity based on COVID-19 genome-wide association studies. Our foundational dataset elucidates the biological effect of severe SARS-CoV-2 infection across the body, a key step towards new treatments. Single-cell analysis of lung, heart, kidney and liver autopsy samples shows the molecular and cellular changes and immune response resulting from severe COVID-19 infection.

The SARS-CoV-2 pandemic has caused over 1 million deaths globally, mostly due to acute lung injury and acute respiratory distress syndrome, or direct complications resulting in multiple-organ failures. Little is known about the host tissue immune and cellular responses associated with COVID-19 infection, symptoms, and lethality. To address this, we collected tissues from 11 organs during the clinical autopsy of 17 individuals who succumbed to COVID-19, resulting in a tissue bank of approximately 420 specimens. We generated comprehensive cellular maps capturing COVID-19 biology related to patients' demise through single-cell and single-nucleus RNA-Seq of lung, kidney, liver and heart tissues, and further contextualized our findings through spatial RNA profiling of distinct lung regions. We developed a computational framework that incorporates removal of ambient RNA and automated cell type annotation to facilitate comparison with other healthy and diseased tissue atlases. In the lung, we uncovered significantly altered transcriptional programs within the epithelial, immune, and stromal compartments and cell intrinsic changes in multiple cell types relative to lung tissue from healthy controls. We observed evidence of: alveolar type 2 (AT2) differentiation replacing depleted alveolar type 1 (AT1) lung epithelial cells, as previously seen in fibrosis; a concomitant increase in myofibroblasts reflective of defective tissue repair; and, putative TP63+ intrapulmonary basal-like progenitor (IPBLP) cells, similar to cells identified in H1N1 influenza, that may serve as an emergency cellular reserve for severely damaged alveoli. Together, these findings suggest the activation and failure of multiple avenues for regeneration of the epithelium in these terminal lungs. SARS-CoV-2 RNA reads were enriched in lung mononuclear phagocytic cells and endothelial cells, and these cells expressed distinct host response transcriptional programs. We corroborated the compositional and transcriptional changes in lung tissue through spatial analysis of RNA profiles in situ and distinguished unique tissue host responses between regions with and without viral RNA, and in COVID-19 donor tissues relative to healthy lung. Finally, we analyzed genetic regions implicated in COVID-19 GWAS with transcriptomic data to implicate specific cell types and genes associated with disease severity. Overall, our COVID-19 cell atlas is a foundational dataset to better understand the biological impact of SARS-CoV-2 infection across the human body and empowers the identification of new therapeutic interventions and prevention strategies.

Abstract Breast cancers are complex cellular ecosystems where heterotypic interactions play central roles in disease progression and response to therapy. However, our knowledge of the cellular composition and organization of breast cancer remains limited. We present a comprehensive single cell and spatially resolved transcriptomic atlas of human breast cancers. The 10X Genomics Chromium platform was used to generate single cell transcriptomic data (scRNA-Seq) from more than 120,000 cells sampled from 26 breast cancers. CITE-Seq was employed to simultaneously generate protein measurements using a panel of 157 antibodies against immune, stromal and epithelial cell surface markers and analysed using Seurat. Using single cell signatures, we estimated the cellular composition of more than 2000 breast cancers in the Metabric cohort using deconvolution methods. Spatial transcriptomics was conducted on 12 frozen tissues (Luminal, Her2+ and triple negative breast cancer (TNBC)) using the 10X genomics Visium solution. We also used a novel Spatial Whole Transcriptome Panel, targeting 18,000+ genes on the Nanostring GeoMX platform, to profile T cells and malignant cells across multiple tissue niches from 16 TNBC FFPE cases. Integrative scRNA-Seq analysis identifies recurrent gene modules driving neoplastic cell heterogeneity, including interferon signaling, estrogen receptor function and mutually exclusive patterns of proliferation versus EMT. We also develop a single cell classifier of intrinsic subtype (scSubtype) to reveal frequent intra-tumoral heterogeneity for breast cancer intrinsic subtypes. CITE-Seq revealed immune profiles at high resolution, leading to the identification of novel macrophage populations with high expression of PD-L1 and PD-L2 immune checkpoint ligands and associations with clinical outcome. We also observe enrichment of exhausted and proliferative CD8 T cells in TNBC, with unique patterns of cell-surface checkpoint protein expression when compared to other subtypes. Targeted analysis using the GeoMX revealed spatial segregation of T cell phenotypes, with exhausted and proliferative CD8 T cells forming small clusters adjacent to tumor cells with high interferon pathway activity. Analysis of scRNA-Seq data revealed that stromal cells generate diverse functions and cell surface protein expression through differentiation within 3 major lineages: fibroblast, endothelial and perivascular-like. Subsets of stromal cells had features associated with immune regulation and Visium data revealed that stromal-immune niches were spatially organized in tumors, offering insights into anti-tumor immune suppression by stromal cells. Finally, deconvolution stratified >2000 breast cancer cases in Metabric into nine clusters, termed ‘ecotypes', with distinct cellular compositions and clinical outcomes. This study provides a comprehensive atlas of the cellular architecture of breast cancer. Citation Format: Sunny Z. Wu, Daniel Roden, Ghamdan Al Eryani, Simon Junankar, Elgene Lim, Aatish Thennavan, Alma Andersson, Stephen Williams, Jingjing Gong, Robin Fropf, Kit Fuhrman, Joakim Lundeberg, Chuck Perou, Alexander Swarbrick. An integrated multi-omic cellular atlas of human breast cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 129.

Spatial genomic technologies can map gene expression in tissues, but provide limited potential for transcriptome-wide discovery approaches and application to fixed tissue samples. Here, we introduce the GeoMX Whole Transcriptome Atlas (WTA), a new technology for transcriptome-wide spatial profiling of tissues with cellular resolution. WTA significantly expands the Digital Spatial Profiling approach to enable in situ hybridisation against 18,190 genes at high-throughput using a sequencing readout. We applied WTA to generate the first spatial transcriptomic map of the fetal human neocortex, validating transcriptome-wide spatial profiling on formalin-fixed tissue material and demonstrating the spatial enrichment of autism gene expression in deep cortical layers. To demonstrate the value of WTA for cell atlasing, we integrated single-cell RNA-sequencing (scRNA-seq) and WTA data to spatially map dozens of neural cell types and showed that WTA can be used to directly measure cell type specific transcriptomes in situ. Moreover, we developed computational tools for background correction of WTA data and accurate integration with scRNA-seq. Our results present WTA as a versatile transcriptome-wide discovery tool for cell atlasing and fixed tissue spatial transcriptomics.

Abstract The emerging field of spatial genomics represents a significant advance for biology. To drive new discoveries in spatial genomics and immuno-oncology, we introduce the GeoMx Cancer Transcriptome Atlas (CTA) Panel for comprehensive spatial analysis of cancer pathways using the Nanostring GeoMx Digital Spatial Profiler (DSP). We demonstrate profiling of 1600+ immuno-oncology targets in the tumor, microenvironment, and immune compartments of archival FFPE tissue sections, coupled to downstream Next Generation Sequencing (NGS) readout to enable high-throughput workflows. High-plex spatial RNA molecular profiling with GeoMx CTA was performed as follows: 1. Photocleavable DNA oligonucleotides tags were coupled to 8000+ in situ hybridization probes targeting 1600+ genes. These reagents were allowed to bind targets directly on slide-mounted FFPE tissue sections. 2. ROIs were identified and selected using GeoMx DSP, and ROI-specific oligonucleotide tags were released using ultraviolet exposure. 3. Released oligonucleotide tags from each ROI were collected and deposited into designated wells on a microtiter plate, allowing well indexing of each ROI during NGS library preparation. 4. After indexing, the entire plate was pooled into a single tube for purification and then sequenced on an Illumina instrument. 5. NGS reads were processed into digital counts and mapped back to each ROI, generating a map of transcript activity within the tissue architecture. We compared data from experiments in which bulk RNA-seq and GeoMx DSP using the CTA Panel were performed on the same samples. Overall, we found good correlation between pseudo-bulk GeoMx CTA (sum of ROIs) and RNA-seq from the same tissue specimen. Individually, however, each ROI showed a distinct expression pattern from bulk, and ROI expression patterns clustered based on similar tissue morphology. Importantly, GeoMx CTA was able to detect a higher number of genes with low expression within the microenvironment and immune spatial compartment compared to bulk RNA-seq, providing a detailed look at the anti-tumor immune response. Lastly, we profiled similar tissues using a novel 18000+ gene whole transcriptome panel and found further enrichment of low-expressers relative to RNA-seq, revealing novel spatial biology previously masked by bulk assays. Together, these data demonstrate that GeoMx offers high sensitivity for genome-scale expression profiling while preserving critical information about tissue architecture. GeoMx DSP technology is for Research Use Only and not for use in diagnostic procedures. Citation Format: Margaret L. Hoang, Michelle Kriner, Zoey Zhou, Zach Norgaard, Kristina Sorg, Chris Merritt, Erin Piazza, Marty Ross, Robin Fropf, Nileshi Saraf, Patrick Danaher, Michael Rhodes, Joseph Beechem. Spatially-resolved in situ expression profiling using the GeoMx™ Cancer Transcriptome Atlas panel in FFPE tissue [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1364.

ABSTRACT Pancreatic ductal adenocarcinoma (PDAC) remains a treatment-refractory disease. Characterizing PDAC by mRNA profiling remains particularly challenging. Previously identified bulk expression subtypes were influenced by contaminating stroma and have not yet informed clinical management, whereas single cell RNA-seq (scRNA-seq) of fresh tumors under-represented key cell types. Here, we developed a robust single-nucleus RNA-seq (snRNA-seq) technique for frozen archival PDAC specimens and used it to study both untreated tumors and those that received neoadjuvant chemotherapy and radiotherapy (CRT). Gene expression programs learned across untreated malignant cell and fibroblast profiles uncovered a clinically relevant molecular taxonomy with improved prognostic stratification compared to prior classifications. Moreover, in the increasingly-adopted neoadjuvant treatment context, there was a depletion of classical-like phenotypes in malignant cells in favor of basal-like phenotypes associated with TNF-NFkB and interferon signaling as well as the presence of novel acinar and neuroendocrine classical-like states, which may be more resilient to cytotoxic treatment. Spatially-resolved transcriptomics revealed an association between malignant cells expressing these basal-like programs and higher immune infiltration with increased lymphocytic content, whereas those exhibiting classical-like programs were linked to sparser macrophage-predominant microniches, perhaps pointing to susceptibility to distinct therapeutic strategies. Our refined molecular taxonomy and spatial resolution can help advance precision oncology in PDAC through informative stratification in clinical trials and insights into differential therapeutic targeting leveraging the immune system.

Genes are regulated through enhancer sequences, in which transcription factor binding motifs and their specific arrangements (syntax) form a cis-regulatory code. To understand the relationship between motif syntax and transcription factor binding, we train a deep learning model that uses DNA sequence to predict base-resolution binding profiles of four pluripotency transcription factors Oct4, Sox2, Nanog, and Klf4. We interpret the model to accurately map hundreds of thousands of motifs in the genome, learn novel motif representations and identify rules by which motifs and syntax influence transcription factor binding. We find that instances of strict motif spacing are largely due to retrotransposons, but that soft motif syntax influences motif interactions at protein and nucleosome range. Most strikingly, Nanog binding is driven by motifs with a strong preference for ∼10.5 bp spacings corresponding to helical periodicity. Interpreting deep learning models applied to high-resolution binding data is a powerful and versatile approach to uncover the motifs and syntax of cis-regulatory sequences.

Circadian regulation is a conserved phenomenon across the animal kingdom, and its disruption can have severe behavioral and physiological consequences. Core circadian clock proteins are likewise well conserved from Drosophila to humans. While the molecular clock interactions that regulate circadian rhythms have been extensively described, additional roles for clock genes during complex behaviors are less understood. Here, we show that mutations in the clock gene period result in differential time-of-day effects on acquisition and long-term memory of aversive olfactory conditioning. Sleep is also altered in period mutants: while its overall levels don't correlate with memory, sleep plasticity in different genotypes correlates with immediate performance after training. We further describe distinct anatomical bases for Period function by manipulating Period activity in restricted brain cells and testing the effects on specific aspects of memory and sleep. In the null mutant background, different features of sleep and memory are affected when we reintroduce a form of the period gene in glia, lateral neurons, and the fan-shaped body. Our results indicate that the role of the clock gene period may be separable in specific aspects of sleep or memory; further studies into the molecular mechanisms of these processes suggest independent neural circuits and molecular cascades that mediate connections between the distinct phenomena.

Understanding how the brain captures transient experience and converts it into long lasting changes in neural circuits requires the identification and investigation of the specific ensembles of neurons that are responsible for the encoding of each experience. We have developed a Robust Activity Marking (RAM) system that allows for the identification and interrogation of ensembles of neurons. The RAM system provides unprecedented high sensitivity and selectivity through the use of an optimized synthetic activity-regulated promoter that is strongly induced by neuronal activity and a modified Tet-Off system that achieves improved temporal control. Due to its compact design, RAM can be packaged into a single adeno-associated virus (AAV), providing great versatility and ease of use, including application to mice, rats, flies, and potentially many other species. Cre-dependent RAM, CRAM, allows for the study of active ensembles of a specific cell type and anatomical connectivity, further expanding the RAM system's versatility.

Many biological phenomena oscillate under the control of the circadian system, exhibiting peaks and troughs of activity across the day/night cycle. In most animal models, memory formation also exhibits this property, but the underlying neuronal and molecular mechanisms remain unclear. The dCREB2 transcription factor shows circadian regulated oscillations in its activity, and has been shown to be important for both circadian biology and memory formation. We show that the time-of-day (TOD) of behavioral training affects Drosophila memory formation. dCREB2 exhibits complex changes in protein levels across the daytime and nighttime, and these changes in protein abundance are likely to contribute to oscillations in dCREB2 activity and TOD effects on memory formation.

CREB-responsive transcription has an important role in adaptive responses in all cells and tissue. In the nervous system, it has an essential and well established role in long-term memory formation throughout a diverse set of organisms. Activation of this transcription factor correlates with long-term memory formation and disruption of its activity interferes with this process. Most convincingly, augmenting CREB activity in a number of different systems enhances memory formation. In Drosophila, a sequence rearrangement in the original transgene used to enhance memory formation has been a source of confusion. This rearrangement prematurely terminates translation of the full-length protein, leaving the identity of the "enhancing molecule" unclear. In this report, we show that a naturally occurring, downstream, in-frame initiation codon is used to make a dCREB2 protein off of both transgenic and chromosomal substrates. This protein is a transcriptional activator and is responsible for memory enhancement. A number of parameters can affect enhancement, including the short-lived activity of the activator protein, and the time-of-day when induction and behavioral training occur. Our results reaffirm that overexpression of a dCREB2 activator can enhance memory formation and illustrate the complexity of this behavioral enhancement.

The transcription factor CREB is an important regulator of many adaptive processes in neurons, including sleep, cellular homeostasis, and memory formation. The Drosophila dCREB2 family includes multiple protein isoforms generated from a single gene. Overexpression of an activator or blocker isoform has been shown to enhance or block memory formation, but the molecular mechanisms underlying these phenomena remain unclear. In the present study, we generate isoform-specific antibodies and new transgenic flies to track and manipulate the activity of different dCREB2 isoforms during memory formation. We find that nuclear accumulation of a dCREB2 activator-related species, p35+, is dynamically regulated during memory formation. Furthermore, various dCREB2 genetic manipulations that enhance or block memory formation correspondingly increase or decrease p35+ levels in the nucleus. Finally, we show that overexpression of S6K can enhance memory formation and increase p35+ nuclear abundance. Taken together, these results suggest that regulation of dCREB2 localization may be a key molecular convergence point in the coordinated host of events that lead to memory formation.

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