Author
Kasper Karlsson
Other affiliations: Karolinska Institutet
Bio: Kasper Karlsson is an academic researcher from Stanford University. The author has contributed to research in topics: Organoid & Carcinogenesis. The author has an hindex of 10, co-authored 12 publications receiving 1609 citations. Previous affiliations of Kasper Karlsson include Karolinska Institutet.
Topics: Organoid, Carcinogenesis, Biology, Cancer, Phenotype
Papers
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TL;DR: Unique molecular identifiers (UMIs), which make each molecule in a population distinct, are applied to genome-scale human karyotyping and mRNA sequencing in Drosophila melanogaster to improve accuracy of almost any next-generation sequencing method.
Abstract: Unique molecular identifiers (UMIs) associate distinct sequences with every DNA or RNA molecule and can be counted after amplification to quantify molecules in the original sample. Using UMIs, the authors obtain a digital karyotype of an individual with Down's syndrome and quantify mRNA in Drosophila melanogaster cells.
1,032 citations
TL;DR: Air-liquid interface method propagated patient-derived organoids (PDOs) from >100 human biopsies or mouse tumors in syngeneic immunocompetent hosts as tumor epithelia with native embedded immune cells to enable immuno-oncology investigations within the TME and facilitate personalized immunotherapy testing.
762 citations
Harvard University1, Johns Hopkins University2, Oregon Health & Science University3, Columbia University4, Broad Institute5, Emory University6, Fred Hutchinson Cancer Research Center7, University of California, San Diego8, Stanford University9, University of California, San Francisco10, University of Texas MD Anderson Cancer Center11
TL;DR: A framework is described for this expanded list of cancer targets, providing novel opportunities for clinical translation and indicating that the diversity of therapeutic targets engendered by non-oncogene dependencies is much larger than the list of recurrently mutated genes.
110 citations
18 Aug 2020
TL;DR: Recent advances in in vitro physiologic systems that model tissues of origin more accurately than classical culture approaches and their applications in cancer biology, clinical translation and precision medicine are described.
Abstract: Organoid technologies enable the creation of in vitro physiologic systems that model tissues of origin more accurately than classical culture approaches. Seminal characteristics of these systems, including three-dimensional structure and recapitulation of self-renewal, differentiation and disease pathology, render organoids eminently suited as hybrids that combine the experimental tractability of traditional two-dimensional cell lines with cellular attributes of in vivo model systems. Here we describe recent advances in this rapidly evolving field and their application to cancer biology, clinical translation and precision medicine. Kuo and colleagues review the application of organoids to study cancer biology and pursue clinical translation and precision medicine.
81 citations
TL;DR: In this article, the authors proposed to use unique molecular identifiers (umis) which make each molecule in the sample distinct, which can be used to detect individual molecules without copying them, and even harder to make defined number of copies of molecules.
Abstract: Advances in molecular biology have made it easy to identify different DNA or RNA species and to copy them. Identification of nucleic acid species can be accomplished by reading the DNA sequence; currently millions of molecules can be sequenced in a single day using massively parallel sequencing. Efficient copying of DNA-molecules of arbitrary sequence was made possible by molecular cloning, and the polymerase chain reaction. Differences in the relative abundance of a large number of different sequences between two or more samples can in turn be measured using microarray hybridization and/or tag sequencing. However, determining the relative abundance of two different species and/or the absolute number of molecules present in a single sample has proven much more challenging. This is because it is hard to detect individual molecules without copying them, and even harder to make defined number of copies of molecules. We show here that this limitation can be overcome by using unique molecular identifiers (umis), which make each molecule in the sample distinct.
68 citations
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TL;DR: Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together.
5,506 citations
01 May 2015
TL;DR: Drop-seq as discussed by the authors analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts' cell of origin, and identifies 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes.
Abstract: Cells, the basic units of biological structure and function, vary broadly in type and state. Single-cell genomics can characterize cell identity and function, but limitations of ease and scale have prevented its broad application. Here we describe Drop-seq, a strategy for quickly profiling thousands of individual cells by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together. Drop-seq analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts' cell of origin. We analyzed transcriptomes from 44,808 mouse retinal cells and identified 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes. Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution. VIDEO ABSTRACT.
3,365 citations
TL;DR: Large-scale single-cell RNA sequencing is used to classify cells in the mouse somatosensory cortex and hippocampal CA1 region and found 47 molecularly distinct subclasses, comprising all known major cell types in the cortex.
Abstract: The mammalian cerebral cortex supports cognitive functions such as sensorimotor integration, memory, and social behaviors. Normal brain function relies on a diverse set of differentiated cell types, including neurons, glia, and vasculature. Here, we have used large-scale single-cell RNA sequencing (RNA-seq) to classify cells in the mouse somatosensory cortex and hippocampal CA1 region. We found 47 molecularly distinct subclasses, comprising all known major cell types in the cortex. We identified numerous marker genes, which allowed alignment with known cell types, morphology, and location. We found a layer I interneuron expressing Pax6 and a distinct postmitotic oligodendrocyte subclass marked by Itpr2. Across the diversity of cortical cell types, transcription factors formed a complex, layered regulatory code, suggesting a mechanism for the maintenance of adult cell type identity.
2,675 citations
TL;DR: All of the major steps in RNA-seq data analysis are reviewed, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping.
Abstract: RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics.
1,963 citations
TL;DR: Low-error sequencing data suggest that initial microbial colonizers of infant guts could persist over the life span of an individual, and members of Bacteroidetes and Actinobacteria are significantly more stable components than the population average.
Abstract: A low-error 16S ribosomal RNA amplicon sequencing method, in combination with whole-genome sequencing of >500 cultured isolates, was used to characterize bacterial strain composition in the fecal microbiota of 37 U.S. adults sampled for up to 5 years. Microbiota stability followed a power-law function, which when extrapolated suggests that most strains in an individual are residents for decades. Shared strains were recovered from family members but not from unrelated individuals. Sampling of individuals who consumed a monotonous liquid diet for up to 32 weeks indicated that changes in strain composition were better predicted by changes in weight than by differences in sampling interval. This combination of stability and responsiveness to physiologic change confirms the potential of the gut microbiota as a diagnostic tool and therapeutic target.
1,641 citations