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Atlas level scRNAseq analysis reveals the functional landscape of cell types
Get PDFCharacterizing the diversity and function of the many cells that make up living organisms is an essential aspect of cell biology. This thesis uses meta-analysis of large-scale single cell RNA sequencing (scRNAseq) data to both explore the diversity of cell types and the functional landscape that defines them. My methodology is based on meta-analysis because it serves to find robust signatures across datasets with significant technical variation, thereby determining what signatures and properties are likely to generalize to new data. After the introduction, I present two vignettes that explore the functional landscape of hematopoiesis Chapter 2 and neurons Chapter 3 using this philosophy. In Chapter 4 I summarize my results and provide a perspective on how the field can build on this work. Finally, Appendix A and B are two manuscripts I contributed to that are relevant, but not central to this work
Innate Immunity and Cancer Pathophysiology
No full textChronic inflammation increases the risk of several cancers, including gastric, colon, and hepatic cancers. Conversely, tumors, similar to tissue injury, trigger an inflammatory response coordinated by the innate immune system. Cellular and molecular mediators of inflammation modulate tumor growth directly and by influencing the adaptive immune response. Depending on the balance of immune cell types and signals within the tumor microenvironment, inflammation can support or restrain the tumor. Adding to the complexity, research from the past two decades has revealed that innate immune cells are highly heterogeneous and plastic, with variable phenotypes depending on tumor type, stage, and treatment. The field is now on the cusp of being able to harness this wealth of data to (a) classify tumors on the basis of their immune makeup, with implications for prognosis, treatment choice, and clinical outcome, and (b) design therapeutic strategies that activate antitumor immune responses by targeting innate immune cells. Expected final online publication date for the Annual Review of Pathology: Mechanisms of Disease, Volume 17 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates
Genomic and experimental evidence that ALK<sup>ATI</sup> does not predict single agent sensitivity to ALK inhibitors
Get PDFGenomic data can facilitate personalized treatment decisions by enabling therapeutic hypotheses in individual patients. Mutual exclusivity has been an empirically useful signal for identifying activating mutations that respond to single agent targeted therapies. However, a low mutation frequency can underpower this signal for rare variants. We develop a resampling based method for the direct pairwise comparison of conditional selection between sets of gene pairs. We apply this method to a transcript variant of anaplastic lymphoma kinase (ALK) in melanoma, termed ALKATI that was suggested to predict sensitivity to ALK inhibitors and we find that it is not mutually exclusive with key melanoma oncogenes. Furthermore, we find that ALKATI is not likely to be sufficient for cellular transformation or growth, and it does not predict single agent therapeutic dependency. Our work strongly disfavors the role of ALKATI as a targetable oncogenic driver that might be sensitive to single agent ALK treatment
BAF complexes drive proliferation and block myogenic differentiation in fusion-positive rhabdomyosarcoma
Get PDFRhabdomyosarcoma (RMS) is a pediatric malignancy of skeletal muscle lineage. The aggressive alveolar subtype is characterized by t(2;13) or t(1;13) translocations encoding for PAX3- or PAX7-FOXO1 chimeric transcription factors, respectively, and are referred to as fusion positive RMS (FP-RMS). The fusion gene alters the myogenic program and maintains the proliferative state while blocking terminal differentiation. Here, we investigated the contributions of chromatin regulatory complexes to FP-RMS tumor maintenance. We define the mSWI/SNF functional repertoire in FP-RMS. We find that SMARCA4 (encoding BRG1) is overexpressed in this malignancy compared to skeletal muscle and is essential for cell proliferation. Proteomic studies suggest proximity between PAX3-FOXO1 and BAF complexes, which is further supported by genome-wide binding profiles revealing enhancer colocalization of BAF with core regulatory transcription factors. Further, mSWI/SNF complexes localize to sites of de novo histone acetylation. Phenotypically, interference with mSWI/SNF complex function induces transcriptional activation of the skeletal muscle differentiation program associated with MYCN enhancer invasion at myogenic target genes, which is recapitulated by BRG1 targeting compounds. We conclude that inhibition of BRG1 overcomes the differentiation blockade of FP-RMS cells and may provide a therapeutic strategy for this lethal childhood tumor
Discovery of Protein Modifications Using Differential Tandem Mass Spectrometry Proteomics
No full textBarcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel in situ analyses.
Get PDFWe present barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel insitu analyses (BOLORAMIS), a reverse transcription-free method for spatially-resolved, targeted, in situ RNA identification of single or multiple targets. BOLORAMIS was demonstrated on a range of cell types and human cerebral organoids. Singleplex experiments to detect coding and non-coding RNAs in human iPSCs showed a stem-cell signature pattern. Specificity of BOLORAMIS was found to be 92% as illustrated by a clear distinction between human and mouse housekeeping genes in a co-culture system, as well as by recapitulation of subcellular localization of lncRNA MALAT1. Sensitivity of BOLORAMIS was quantified by comparing with single molecule FISH experiments and found to be 11%, 12% and 35% for GAPDH, TFRC and POLR2A, respectively. To demonstrate BOLORAMIS for multiplexed gene analysis, we targeted 96 mRNAs within a co-culture of iNGN neurons and HMC3 human microglial cells. We used fluorescence in situ sequencing to detect error-robust 8-base barcodes associated with each of these genes. We then used this data to uncover the spatial relationship among cells and transcripts by performing single-cell clustering and gene-gene proximity analyses. We anticipate the BOLORAMIS technology for in situ RNA detection to find applications in basic and translational research
Genotype-environment mismatch of kelp forests under climate change.
No full textClimate change is increasingly impacting ecosystems globally. Understanding adaptive genetic diversity and whether it will keep pace with projected climatic change is necessary to assess species' vulnerability and design efficient mitigation strategies such as assisted adaptation. Kelp forests are the foundations of temperate reefs globally but are declining in many regions due to climate stress. A lack of knowledge of kelps' adaptive genetic diversity hinders assessment of vulnerability under extant and future climates. Using 4245 single nucleotide polymorphisms (SNPs), we characterised patterns of neutral and putative adaptive genetic diversity for the dominant kelp in the southern hemisphere (Ecklonia radiata) from ~1000 km coastline off Western Australia. Strong population structure and isolation-by-distance was underpinned by significant signatures of selection related to temperature and light. Gradient forest analysis of temperature-linked SNPs under selection revealed a strong association with mean annual temperature range suggesting adaptation to local thermal environments. Critically, modelling revealed that predicted climate-mediated temperature changes will likely result in high genomic vulnerability via a mismatch between current and future predicted genotype-environment relationships such that kelp forests off Western Australia will need to significantly adapt to keep pace with projected climate change. Proactive management techniques such as assisted adaptation to boost resilience may be required to secure the future of these kelp forests and the immense ecological and economic values they support
Radiomics for Renal Cell Carcinoma: Predicting Outcomes from Immunotherapy and Targeted Therapies—A Narrative Review
No full textT-cell immunotherapy and molecular targeted therapies have become standard-of-care treatments for renal cell carcinoma (RCC). There is a need to develop robust biomarkers that predict patient outcomes to targeted therapies to personalise treatment. In recent years, quantitative analysis of imaging features, termed radiomics, has been used to extract tumour features. This narrative mini review summarises the evidence for radiomics prediction of immunotherapy and molecular targeted therapy outcomes in RCC. Radiomics may predict survival, treatment response, and disease progression in RCC treated with tyrosine kinase inhibitors (eg, sunitinib) and immune checkpoint inhibitors (eg, nivolumab). Further validation is necessary in large-scale studies. Patient summary: We summarise evidence on the ability of features extracted from CT (computed tomography) scans to predict patient outcomes from new treatments for kidney cancer. Although these features can predict treatment outcomes for patients, including survival, treatment response, and cancer progression, further research is necessary before this technology can be applied clinically
AdRoit is an accurate and robust method to infer complex transcriptome composition
Get PDFBulk RNA sequencing provides the opportunity to understand biology at the whole transcriptome level without the prohibitive cost of single cell profiling. Advances in spatial transcriptomics enable to dissect tissue organization and function by genome-wide gene expressions. However, the readout of both technologies is the overall gene expression across potentially many cell types without directly providing the information of cell type constitution. Although several in-silico approaches have been proposed to deconvolute RNA-Seq data composed of multiple cell types, many suffer a deterioration of performance in complex tissues. Here we present AdRoit, an accurate and robust method to infer the cell composition from transcriptome data of mixed cell types. AdRoit uses gene expression profiles obtained from single cell RNA sequencing as a reference. It employs an adaptive learning approach to alleviate the sequencing technique difference between the single cell and the bulk (or spatial) transcriptome data, enhancing cross-platform readout comparability. Our systematic benchmarking and applications, which include deconvoluting complex mixtures that encompass 30 cell types, demonstrate its preferable sensitivity and specificity compared to many existing methods as well as its utilities. In addition, AdRoit is computationally efficient and runs orders of magnitude faster than most methods
The diversity and specificity of functional connectivity across spatial and temporal scales
Get PDFMacroscopic neuroimaging modalities in humans have revealed the organization of brain-wide activity into distributed functional networks that re-organize according to behavioral demands. However, the inherent coarse-graining of macroscopic measurements conceals the diversity and specificity in responses and connectivity of many individual neurons contained in each local region. New invasive approaches in animals enable recording and manipulating neural activity at meso- and microscale resolution, with cell-type specificity and temporal precision down to milliseconds. Determining how brain-wide activity patterns emerge from interactions across spatial and temporal scales will allow us to identify the key circuit mechanisms contributing to global brain states and how the dynamic activity of these states enables adaptive behavior