The Pediatric Cell Atlas: defining the growth phase of human development at single-cell resolution

Single-cell gene expression analyses of mammalian tissues have uncovered profound stage-specific molecular regulatory phenomena that have changed the understanding of unique cell types and signaling pathways critical for lineage determination, morphogenesis, and growth. We discuss here the case for a Pediatric Cell Atlas as part of the Human Cell Atlas consortium to provide single-cell profiles and spatial characterization of gene expression across human tissues and organs. Such data will complement adult and developmentally focused HCA projects to provide a rich cytogenomic framework for understanding not only pediatric health and disease but also environmental and genetic impacts across the human lifespan

HOLOBIOMICS - Use of microbiomics for the exploration of microbial communities in holobionts.

Introducing more than a decade ago the High-Throughput Sequencing techniques we have exponentially increased our opportunities of shedding light on complex microbial communities. This revolution opened a ‘golden era’ in the new-born field of microbiomics, avoiding the culturing step that always represented a limiting factor in the characterization of particular and fastidious groups of microorganisms. Furthermore, it is clear the advantage of retrieving all the taxonomic and functional information encoded within a microbiome directly by sequencing a sample deriving from an environment of interest. The huge amount of information produced in studies relying on NGS represents a challenging task, constituting the main driver for the creation of the computational microbiologist: a new figure alongside the molecular microbiologist and classic microbiologist. This researcher’s work starts when the laboratory work ends and the sequencing process is completed: the aim of a computational microbiologist work is to deal with the vast amount of data generated by the sequencing process, producing biologically meaningful data. During my PhD I have focused on these latter tasks, dealing with the characterization at different levels of various holobionts, ranging from wild animals to humans, giving attention at the bacterial, fungal and viral fractions in ecosystems. In the present work I report the main achievements of my research work, whose common denominator is the bioinformatic approach to microbiome data. In the cases I studied, I observed a mutualistic microbiome that may follows adaptive strategies aimed at the conservation of the homeostasis of the total ecosystem. This work contributes to enrich the overall knowledge on the holobiont, also exploring some peculiar ecosystems for the first time. The data presented here may form the basis for future developments in the field, in order to obtain a more comprehensive profiling of bacterial, viral and fungal fractions within complex ecosystems

Doctor of Philosophy

dissertationAdvances in sequencing technologies have made it possible to generate large amounts of microbiological sequence data without culture methods. The data generated pose a significant data analysis challenge. This is especially true in clinical diagnostics where accurate and timely diagnoses are key. To enable infectious disease diagnostics, we created Taxonomer, a kmer-based metagenomics software tool, which can rapidly process large amounts of sequence data with accuracy and precision similar to slower alignment-based approaches. A kmer is a nucleotide subsequence of k length. Kmer exact matching is performed in RAM, utilizing data structures with rapid query times, making kmer approaches magnitudes faster than alignment methods. Prior to Taxonomer, other kmer-based methods were subject to high false positive rates. Taxonomer differs by 1) providing a workflow that reduces false-positives, 2) including host-transcript profiling, and 3) providing a novel protein kmer tool to identify viruses, which are typically too divergent to reliably identify using nucleotide sequence. A web-based front-end was created with the D3 enabled iobio framework. Reference sets utilized in Taxonomer were obtained from NCBI, GreenGenes, unite, and uniprot databases. A wide-range of simulated datasets and real clinical specimens were created or obtained to evaluate Taxonomer. Taxonomer was compared to previously published pipelines (SURPI), classifiers (Kraken, RDP classifier), and sequence alignment methods (BLAST, SNAP, RapSearch2, DIAMOND). Taxonomer was also iv compared to a commercially available respiratory virus panel and utilized on a large cohort of pneumonia positive patients that had previously undergone extensive microbiological diagnostics. Taxonomer had agreement at 98.7% with SURPI to assign reads at the phylum level. Taxonomer, RDP classifier, and Kraken classified simulated 16S rRNA reads correctly at the species level at 59.5, 61.7, and 46.0%, respectively. Protein classification using reads derived from viruses showed similar sensitivity to alignment-based methods with RapSearch2, and DIAMOND but with slightly decreased analysis times. Taxonomer provides an accurate workflow for processing samples in a diagnostic setting. It identifies bacteria, fungi, virus, and human transcripts from clinical specimens with accuracy comparable to alignment methods. Its web-based front-end makes it accessible to laboratories without significant compute resources

34th Annual Meeting & Pre-Conference Programs of the Society for Immunotherapy of Cancer (SITC 2019): part 2

Document type: Articl

Surviving Oncology: Living With Cancer in the Wake of Integrative Care

This dissertation analyzes the emerging medical field of integrative oncology, attending to how this approach to cancer treatment unsettles and reconfigures existing biomedical ideas about bodies and cancer. Informed by twelve months of multi-sited ethnographic study conducted in the state of California, it examines the attempts made by integrative practitioners to provide whole patient care by incorporating complementary medicines such as Ayurveda and Chinese medicine into conventional oncology. I suggest that this approach enacts a kind of sensitivity for how cancer is lived as a disease conditioned by emotional, psychological, social, and environmental factors, requiring treatments attentive to these dimensions. Throughout this study I grapple with the intentions of integrative oncologists and the realities of the political economy of medicine and insurance in the United States that leaves integrative care out of the reach of most people, producing a situation where many are strained to imagine different ways of surviving oncology. At the core of this project is a concern for what it means and what it takes to live well with cancer in biomedicine