The molecular landscape of neural differentiation in the developing Drosophila brain
      revealed by targeted scRNA-seq and multi-informatic analysis

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Abstract

The Drosophila type II neuroblast lineages present an attractive model to investigate the neurogenesis and differentiation process as they adapt to a process similar to that in the human outer subventricular zone. We perform targeted single-cell mRNA sequencing in third instar larval brains to study this process of the type II NB lineage. Combining prior knowledge, in silico analyses, and in situ validation, our multi-informatic investigation describes the molecular landscape from a single developmental snapshot. 17 markers are identified to differentiate distinct maturation stages. 30 markers are identified to specify the stem cell origin and/or cell division numbers of INPs, and at least 12 neuronal subtypes are identified. To foster future discoveries, we provide annotated tables of pairwise gene-gene correlation in single cells and MiCV, a web tool for interactively analyzing scRNA-seq datasets. Taken together, these resources advance our understanding of the neural differentiation process at the molecular level. Using a combination of targeted scRNA-seq, in situ RNA staining, and a multi-informatic analysis paradigm, Michki et al. characterize the transcriptome landscape of thousands of type II neurons and their progenitors in the developing larval fruit fly brain. <div class="figure-container so-text-align-c"> <img alt="" class="figure" src="/document_file/8cb3ca4f-df8d-4884-baf0-18b70ac17a7d/PubMedCentral/image/nihms-1698688-f0007.jpg"/> </div>

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Most cited references 81

Record: found
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Fiji: an open-source platform for biological-image analysis.

Johannes Schindelin, Ignacio Arganda-Carreras, Erwin Frise … (2019)

Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.

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Record: found
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STAR: ultrafast universal RNA-seq aligner.

Alexander Dobin, Carrie A. Davis, Felix Schlesinger … (2013)

Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

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Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

Da Huang, Brad T. Sherman, Richard A. Lempicki (2009)

DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.