Targeted single-cell RNA sequencing of transcription factors enhances the identification of cell types and trajectories

Pokhilko, Alexandra; Handel, Adam E.; Curion, Fabiola; Volpato, Viola; Whiteley, Emma; Bøstrand, Sunniva M. K.; Newey, Sarah E.; Akerman, Colin J.; Webber, Caleb; Clark, Michael B.; Bowden, Rory; Cader, M Z

doi:10.1101/gr.273961.120

Cited by 19 publications

(7 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The current work extends the previous analysis by looking also at the non-coding portion of the genome. While the inevitably low depth of the scRNA seq approaches somehow limits the identification of weakly expressed molecules like ncRNAs ( Pokhilko et al, 2021 ), we still were able to identify 43 LncRNAs DE in iGCA patients. Also, in light of the extensive spectrum of comorbidities observed in unfertile males ( Salonia et al, 2009 ; Shiraishi and Matsuyama, 2018 ; Boeri et al, 2022 ), the study of regulatory transcripts, like LncRNAs, in this class of patients appears to be of paramount importance.…”

Section: Discussionmentioning

confidence: 93%

Single-cell transcriptome profiling reveals several LncRNAs differentially expressed in idiopathic germ cell aplasia

Lavorgna

Tascini

Bertini

et al. 2022

Front. Cell Dev. Biol.

View full text Add to dashboard Cite

Mechanisms underlying severe male infertility are still largely elusive. However, recently, a single-cell transcription study by our group identified several differentially expressed coding genes in all the somatic cell types in testes of patients with idiopathic germ cell aplasia (iGCA). Here, we leverage this work by extending the analysis also to the non-coding portion of the genome. As a result, we found that 43 LncRNAs were differentially expressed in the somatic cells of these patients. Interestingly, a significant portion of the overexpressed LncRNAs was found to be a target of TAF9B, a transcription factor known to be involved in germ cell survival. Moreover, several overexpressed LncRNAs were also found to be activated in a mouse model of Sertoli cells treated with bisphenol A, a widespread environmental contaminant, long suspected to impair male fertility. Finally, a literature search for MEG3, a maternally imprinted LncRNA overexpressed as well in our patients, found it to be involved, among other things, in obesity and inflammation, known comorbidities of iGCA, ultimately suggesting that our findings deepen the understanding of the molecular insights coupled not only to the pathogenesis, but also to the clinical course of this class of patients.

show abstract

Section: Discussionmentioning

confidence: 93%

Single-cell transcriptome profiling reveals several LncRNAs differentially expressed in idiopathic germ cell aplasia

Lavorgna

Tascini

Bertini

et al. 2022

Front. Cell Dev. Biol.

View full text Add to dashboard Cite

show abstract

“…Molecular counting by the 8 bp SMART-Seq3 UMI can be calibrated by comparing to counts of the 18-bp spike UMIs. DAUMI is not intended for general scRNA-seq data, but it can be applied to this molecular spike data and targeted single-cell data ( Pokhilko et al , 2021 ; Woyke and Jarett, 2015 ). We demultiplexed using ( Galanti et al , 2021 ) and retained only 5′ reads of the molecular spike.…”

Section: Resultsmentioning

confidence: 99%

Accurate estimation of molecular counts from amplicon sequence data with unique molecular identifiers

Peng

Dorman

2023

Bioinformatics

View full text Add to dashboard Cite

Motivation Amplicon sequencing is widely applied to explore heterogeneity and rare variants in genetic populations. Resolving true biological variants and quantifying their abundance is crucial for downstream analyses, but measured abundances are distorted by stochasticity and bias in amplification, plus errors during Polymerase Chain Reaction (PCR) and sequencing. One solution attaches Unique Molecular Identifiers (UMIs) to sample sequences before amplification eliminating amplification bias by clustering reads on UMI and counting clusters to quantify abundance. While modern methods improve over naïve clustering by UMI identity, most do not account for UMI reuse, or collision, and they do not adequately model PCR and sequencing errors in the UMIs and sample sequences. Results We introduce Deduplication and Abundance estimation with UMIs (DAUMI), a probabilistic framework to detect true biological amplicon sequences and accurately estimate their deduplicated abundance. DAUMI recognizes UMI collision, even on highly similar sequences, and detects and corrects most PCR and sequencing errors in the UMI and sampled sequences. DAUMI performs better on simulated and real data compared to other UMI-aware clustering methods. Availability Source code is available at https://github.com/DormanLab/AmpliCI. Supplementary information Supplementary material are available at Bioinformatics online.

show abstract

“…We identified significant differences in the expression of hundreds of genes including key transcription factors such as ELAVL4 and NHLH1 ( Fig. 3 ), which are known to be upregulated during the differentiation of cortical neurons [21, 22]. Moreover, we find differential gene expression of well-defined neuron specific genes such as NRN1 [23] and PLPPR1 [24] ( Fig.…”

Section: Resultsmentioning

confidence: 99%

Identification of cell barcodes from long-read single-cell RNA-seq with BLAZE

You

Prawer

Paoli-Iseppi

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Single-cell RNA sequencing (scRNA-seq) has revolutionised our ability to profile gene expression. However, short-read (SR) scRNAseq methodologies such as 10x are restricted to sequencing the 3' or 5' ends of transcripts, providing accurate gene expression but little information on the RNA isoforms expressed in each cell. Newly developed long-read (LR) scRNA-seq enables the quantification of RNA isoforms in individual cells but LR scRNA-seq using the Oxford Nanopore platform has largely relied upon matched short-read data to identify cell barcodes and allow single cell analysis. Here we introduce BLAZE (Barcode identification from long-reads for AnalyZing single-cell gene Expression), which accurately and efficiently identifies 10x cell barcodes using only nanopore LR scRNA-seq data. We compared BLAZE to existing tools, including cell barcodes identified from matched SR scRNA-seq, on differentiating stem cells and 5 cancer cell lines. BLAZE outperforms existing tools and provides a more accurate representation of the cells present in LR scRNA-seq than using matched short-reads. BLAZE provides accurate cell barcodes over a wide range of experimental read depths and sequencing accuracies, while other methodologies commonly identify false-positive barcodes and cell clusters, disrupting biological interpretation of LR scRNA-seq results. In conclusion, BLAZE eliminates the requirement for matched SR scRNA-seq to interpret LR scRNA-seq, simplifying procedures and decreasing costs while also improving LR scRNA-seq results. BLAZE is compatible with downstream tools accepting a cell barcode whitelist file and is available at https://github.com/shimlab/BLAZE.

show abstract

Targeted single-cell RNA sequencing of transcription factors enhances the identification of cell types and trajectories

Cited by 19 publications

References 46 publications

Single-cell transcriptome profiling reveals several LncRNAs differentially expressed in idiopathic germ cell aplasia

Single-cell transcriptome profiling reveals several LncRNAs differentially expressed in idiopathic germ cell aplasia

Accurate estimation of molecular counts from amplicon sequence data with unique molecular identifiers

Identification of cell barcodes from long-read single-cell RNA-seq with BLAZE

Contact Info

Product

Resources

About