DeLTa-Seq: direct-lysate targeted RNA-Seq from crude tissue lysate

Kashima, Makoto; Kamitani, Mari; Nomura, Yasuyuki; Hirata, Hiromi; Nagano, Atsushi J.

doi:10.1101/2020.09.15.299180

Cited by 3 publications

(3 citation statements)

References 54 publications

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“…3’ mRNA-Seq was conducted using the Lasy-Seq ver. 1.1 Protocol (https://sites.google.com/view/lasy-seq/) 11, 12 . Briefly, 100 ng of total RNA was reverse transcribed using an RT primer with an index and SuperScript IV reverse transcriptase (Thermo Fisher Scientific, Waltham, MA, USA).…”

Section: Methodsmentioning

confidence: 99%

Tensor Decomposition-based Unsupervised Feature Extraction Succeeded in Identification of Differentially Expressed Transcripts from Redundantde novoTranscriptome ofPlanarian

Kashima

Kumagai

Hirata

et al. 2021

Preprint

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RNA-Seq data analysis of non-model organisms is often difficult because of the lack of a well-annotated genome. In model organisms, after short reads are mapped to the genome, it is possible to focus on the analysis of regions well-annotated regions. However, in non-model organisms, contigs can be generated by de novo assembling. This can result in a large number of transcripts, making it difficult to easily remove redundancy. A large number of transcripts can also lead to difficulty in the recognition of differentially expressed transcripts (DETs) between more than two experimental conditions, because P-values must be corrected by considering multiple comparison corrections whose effect is enhanced as the number of transcripts increases. Heavily corrected P-values often fail to take sufficiently small P-values as significant. In this study, we applied a recently proposed tensor decomposition (TD)-based unsupervised feature extraction (FE) to the RNA-seq data obtained for a non-model organism, Planarian; we successfully obtained a limited number of transcripts whose expression was altered between normal and defective samples as well as during time development. TD-based unsupervised FE is expected to be an effective tool that can identify a limited number of DETs, even when a poorly annotated genome is available.

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Section: Methodsmentioning

confidence: 99%

Tensor Decomposition-based Unsupervised Feature Extraction Succeeded in Identification of Differentially Expressed Transcripts from Redundantde novoTranscriptome ofPlanarian

Kashima

Kumagai

Hirata

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Non targeted RNA-Seq were conducted according to the Lasy-Seq ver. 1.1 protocol (https://sites.google.com/view/lasy-seq/) 21,23 . Briefly, 50 ng of total RNA were reverse transcribed using an RT primer with index and SuperScript IV reverse transcriptase (Thermo Fisher Scientific, Waltham, MA, USA).…”

Section: Methodsmentioning

confidence: 99%

“…Fortunately, recent technical advances related to bulk RNA-Seq have overcome these limitations. Advances in sequencing platforms 18 , RNA extraction method 16,19 and bulk 3′ RNA-Seq library preparation methods 20–22 have enabled a cost-effective time-course individual RNA-Seq 23 (a time-series RNA-Seq targeting a whole embryo or tissue of each individual). Pseudo-time analysis for individual RNA-Seq might capture individual differences in progression speed of biological events such as development.…”

Section: Introductionmentioning

confidence: 99%

Intracellular and intercellular gene regulatory networks inference from time-course individual RNA-Seq

Kashima

Shida

Yamashiro

et al. 2021

Preprint

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Motivation: Recent technical advances in bulk RNA-Seq have enabled time-course RNA-Seq of whole individual embryos to understand the underlying molecular mechanisms. Thus, we hypothesized that gene regulatory networks (GRNs) inferred from time-course individual RNA-Seq during embryonic development reveal intercellular regulatory relationships involved in signaling pathways. Results: Time-course bulk RNA-Seq of individual mouse embryos in early development, followed by pseudo-time analysis and GRN inference, demonstrated that GRN inference from RNA-Seq with pseudo-time can be applied for individual bulk RNA-Seq similar to scRNA-Seq. Validation using an experimental-source-based database showed that our approach could significantly infer GRN for all transcription factors in the database. Furthermore, the inferred ligand-related and receptor-related downstream genes were significantly overlapped. Overall, the inferred GRN include intracellular as well as intercellular regulatory relationships, which cannot be inferred from scRNA-Seq. Thus, inferring GRN from time-course bulk RNA-Seq is a novel approach for understanding the regulatory relationships underlying cellular events.

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Intracellular and Intercellular Gene Regulatory Network Inference From Time-Course Individual RNA-Seq

et al. 2021

Self Cite

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Gene regulatory network (GRN) inference is an effective approach to understand the molecular mechanisms underlying biological events. Generally, GRN inference mainly targets intracellular regulatory relationships such as transcription factors and their associated targets. In multicellular organisms, there are both intracellular and intercellular regulatory mechanisms. Thus, we hypothesize that GRNs inferred from time-course individual (whole embryo) RNA-Seq during development can reveal intercellular regulatory relationships (signaling pathways) underlying the development. Here, we conducted time-course bulk RNA-Seq of individual mouse embryos during early development, followed by pseudo-time analysis and GRN inference. The results demonstrated that GRN inference from RNA-Seq with pseudo-time can be applied for individual bulk RNA-Seq similar to scRNA-Seq. Validation using an experimental-source-based database showed that our approach could significantly infer GRN for all transcription factors in the database. Furthermore, the inferred ligand-related and receptor-related downstream genes were significantly overlapped. Thus, the inferred GRN based on whole organism could include intercellular regulatory relationships, which cannot be inferred from scRNA-Seq based only on gene expression data. Overall, inferring GRN from time-course bulk RNA-Seq is an effective approach to understand the regulatory relationships underlying biological events in multicellular organisms.

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DeLTa-Seq: direct-lysate targeted RNA-Seq from crude tissue lysate

Cited by 3 publications

References 54 publications

Tensor Decomposition-based Unsupervised Feature Extraction Succeeded in Identification of Differentially Expressed Transcripts from Redundantde novoTranscriptome ofPlanarian

Tensor Decomposition-based Unsupervised Feature Extraction Succeeded in Identification of Differentially Expressed Transcripts from Redundantde novoTranscriptome ofPlanarian

Intracellular and intercellular gene regulatory networks inference from time-course individual RNA-Seq

Intracellular and Intercellular Gene Regulatory Network Inference From Time-Course Individual RNA-Seq

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