Single Cell Micro RNA Sequencing Library Preparation.

Micro RNAs represent important post-transcriptional regulators in health and are involved in the onset of many diseases. Therefore, the further characterization of physiological miRNA functions is an important basic research question, and miRNAs even have high potential as biomarkers both for prognosis and diagnosis. In order to exploit this potential, it is mandatory to accurately quantify the miRNA expression not only in bulk but also on the single-cell level. Here, we describe a protocol, which facilitates miRNA sequencing library preparation of very low input samples, single cells, and even clinical samples such as circulating tumor cells. The protocol can be combined with different single-cell isolation methods (e.g., micromanipulation and FACS sorting). After cell lysis, sequencing adapters are ligated to the miRNAs, other ncRNA species, and adapter dimers are reduced by exonuclease digest, the miRNA library is reverse transcribed, amplified, and purified. Furthermore, quality controls are described to select only high-quality samples for sequencing.

Single-cell microRNA sequencing method comparison and application to cell lines and circulating lung tumor cells.

Molecular single cell analyses provide insights into physiological and pathological processes. Here, in a stepwise approach, we first evaluate 19 protocols for single cell small RNA sequencing on MCF7 cells spiked with 1 pg of 1,006 miRNAs. Second, we analyze MCF7 single cell equivalents of the eight best protocols. Third, we sequence single cells from eight different cell lines and 67 circulating tumor cells (CTCs) from seven SCLC patients. Altogether, we analyze 244 different samples. We observe high reproducibility within protocols and reads covered a broad spectrum of RNAs. For the 67 CTCs, we detect a median of 68 miRNAs, with 10 miRNAs being expressed in 90% of tested cells. Enrichment analysis suggested the lung as the most likely organ of origin and enrichment of cancer-related categories. Even the identification of non-annotated candidate miRNAs was feasible, underlining the potential of single cell small RNA sequencing.

The Novel Anaerobiosis-Responsive Overlapping Gene ano Is Overlapping Antisense to the Annotated Gene ECs2385 of Escherichia coli O157:H7 Sakai.

Current notion presumes that only one protein is encoded at a given bacterial genetic locus. However, transcription and translation of an overlapping open reading frame (ORF) of 186 bp length were discovered by RNAseq and RIBOseq experiments. This ORF is almost completely embedded in the annotated L,D-transpeptidase gene ECs2385 of Escherichia coli O157:H7 Sakai in the antisense reading frame -3. The ORF is transcribed as part of a bicistronic mRNA, which includes the annotated upstream gene ECs2384, encoding a murein lipoprotein. The transcriptional start site of the operon resides 38 bp upstream of the ECs2384 start codon and is driven by a predicted σ70 promoter, which is constitutively active under different growth conditions. The bicistronic operon contains a ρ-independent terminator just upstream of the novel gene, significantly decreasing its transcription. The novel gene can be stably expressed as an EGFP-fusion protein and a translationally arrested mutant of ano, unable to produce the protein, shows a growth advantage in competitive growth experiments compared to the wild type under anaerobiosis. Therefore, the novel antisense overlapping gene is named ano (anaerobiosis responsive overlapping gene). A phylostratigraphic analysis indicates that ano originated very recently de novo by overprinting after the Escherichia/Shigella clade separated from other enterobacteria. Therefore, ano is one of the very rare cases of overlapping genes known in the genus Escherichia.

A novel short L-arginine responsive protein-coding gene (laoB) antiparallel overlapping to a CadC-like transcriptional regulator in Escherichia coli O157:H7 Sakai originated by overprinting.

BACKGROUND: Due to the DNA triplet code, it is possible that the sequences of two or more protein-coding genes overlap to a large degree. However, such non-trivial overlaps are usually excluded by genome annotation pipelines and, thus, only a few overlapping gene pairs have been described in bacteria. In contrast, transcriptome and translatome sequencing reveals many signals originated from the antisense strand of annotated genes, of which we analyzed an example gene pair in more detail. RESULTS: A small open reading frame of Escherichia coli O157:H7 strain Sakai (EHEC), designated laoB (L-arginine responsive overlapping gene), is embedded in reading frame -2 in the antisense strand of ECs5115, encoding a CadC-like transcriptional regulator. This overlapping gene shows evidence of transcription and translation in Luria-Bertani (LB) and brain-heart infusion (BHI) medium based on RNA sequencing (RNAseq) and ribosomal-footprint sequencing (RIBOseq). The transcriptional start site is 289 base pairs (bp) upstream of the start codon and transcription termination is 155 bp downstream of the stop codon. Overexpression of LaoB fused to an enhanced green fluorescent protein (EGFP) reporter was possible. The sequence upstream of the transcriptional start site displayed strong promoter activity under different conditions, whereas promoter activity was significantly decreased in the presence of L-arginine. A strand-specific translationally arrested mutant of laoB provided a significant growth advantage in competitive growth experiments in the presence of L-arginine compared to the wild type, which returned to wild type level after complementation of laoB in trans. A phylostratigraphic analysis indicated that the novel gene is restricted to the Escherichia/Shigella clade and might have originated recently by overprinting leading to the expression of part of the antisense strand of ECs5115. CONCLUSIONS: Here, we present evidence of a novel small protein-coding gene laoB encoded in the antisense frame -2 of the annotated gene ECs5115. Clearly, laoB is evolutionarily young and it originated in the Escherichia/Shigella clade by overprinting, a process which may cause the de novo evolution of bacterial genes like laoB.

Discovery of numerous novel small genes in the intergenic regions of the Escherichia coli O157:H7 Sakai genome.

In the past, short protein-coding genes were often disregarded by genome annotation pipelines. Transcriptome sequencing (RNAseq) signals outside of annotated genes have usually been interpreted to indicate either ncRNA or pervasive transcription. Therefore, in addition to the transcriptome, the translatome (RIBOseq) of the enteric pathogen Escherichia coli O157:H7 strain Sakai was determined at two optimal growth conditions and a severe stress condition combining low temperature and high osmotic pressure. All intergenic open reading frames potentially encoding a protein of ≥ 30 amino acids were investigated with regard to coverage by transcription and translation signals and their translatability expressed by the ribosomal coverage value. This led to discovery of 465 unique, putative novel genes not yet annotated in this E. coli strain, which are evenly distributed over both DNA strands of the genome. For 255 of the novel genes, annotated homologs in other bacteria were found, and a machine-learning algorithm, trained on small protein-coding E. coli genes, predicted that 89% of these translated open reading frames represent bona fide genes. The remaining 210 putative novel genes without annotated homologs were compared to the 255 novel genes with homologs and to 250 short annotated genes of this E. coli strain. All three groups turned out to be similar with respect to their translatability distribution, fractions of differentially regulated genes, secondary structure composition, and the distribution of evolutionary constraint, suggesting that both novel groups represent legitimate genes. However, the machine-learning algorithm only recognized a small fraction of the 210 genes without annotated homologs. It is possible that these genes represent a novel group of genes, which have unusual features dissimilar to the genes of the machine-learning algorithm training set.