Meso-seq for in-depth transcriptomics in ultra-low amounts of FACS-purified neuronal nuclei.

Profiling of gene expression in sparse populations of genetically defined neurons is essential for dissecting the molecular mechanisms that control the development and plasticity of neural circuits. However, current transcriptomic approaches are ill suited for detailed mechanistic studies in sparse neuronal populations, as they either are technically complex and relatively expensive (e.g., single-cell RNA sequencing [RNA-seq]) or require large amounts of input material (e.g., traditional bulk RNA-seq). Thus, we established Meso-seq, a meso-scale protocol for identifying more than 10,000 robustly expressed genes in as little as 50 FACS-sorted neuronal nuclei. We demonstrate that Meso-seq works well for multiple neuroscience applications, including transcriptomics in antibody-labeled cortical neurons in mice and non-human primates, analyses of experience-regulated gene programs, and RNA-seq from visual cortex neurons labeled ultra-sparsely with viruses. Given its simplicity, robustness, and relatively low costs, Meso-seq is well suited for molecular-mechanistic studies in ultra-sparse neuronal populations in the brain.

A reproducible and sensitive method for generating high-quality transcriptomes from single whitefly salivary glands and other low-input tissues.

Transcriptomic studies are an important tool for understanding the molecular pathways underlying host plant use by agricultural pests, including vectors of damaging plant pathogens. Thus far, bulk RNA-Seq has been the main approach for non-model insects. This method relies on pooling large numbers of whole organisms or hundreds of individually dissected organs. The latter approach is logistically challenging, may introduce artifacts of handling and storage, and is not compatible with biological replication. Here, we tested an approach to generate transcriptomes of individual salivary glands and other low-input body tissues from whiteflies (Bemisia tabaci MEAM1), which are major vectors of plant viruses. By comparing our outputs to published bulk RNA-Seq datasets for whole whitefly bodies and pools of salivary glands, we demonstrate that this approach recovers similar numbers of transcripts relative to bulk RNA-Seq in a tissue-specific manner, and for some metrics, exceeds performance of bulk tissue RNA-Seq. Libraries generated from individual salivary glands also yielded additional novel transcripts not identified in pooled salivary gland datasets, and had hundreds of enriched transcripts when compared with whole head tissues. Overall, our study demonstrates that it is feasible to produce high quality, replicated transcriptomes of whitefly salivary glands and other low-input tissues. We anticipate that our approach will expand hypothesis-driven research on salivary glands of whiteflies and other Hemiptera, thus enabling novel control strategies to disrupt feeding and virus transmission.

DigiTAG-a RNA Sequencing Approach to Analyze Transcriptomes of Rare Cell Populations in Drosophila melanogaster.

Cell-type specific transcriptional programs underlie the development and maintenance of organs. Not only distinct cell types within a tissue, even cells with supposedly identical cell fates show a high degree of transcriptional heterogeneity. Inevitable, low cell numbers are a major hurdle to study transcriptomes of pure cell populations. Here we describe DigiTAG, a high-throughput method that combines transposase fragmentation and molecular barcoding to retrieve high quality transcriptome data of rare cell types in Drosophila melanogaster. The protocol showcases how DigiTAG can be used to analyse the transcriptome of rare neural stem cells (type II neuroblasts) of Drosophila larval brains, but can also be utilized for other cell types or model systems.

Transcriptome Profiling of Layer 5 Intratelencephalic Projection Neurons From the Mature Mouse Motor Cortex.

The mature cortex contains hugely diverse populations of pyramidal projection neurons (PNs), critical to normal forebrain circuits. In order to understand the healthy cortex, it is essential to characterize this neuronal complexity. We recently demonstrated different identities for Fezf2-positive (Fezf2+ve) and Fezf2-negative (Fezf2-ve) intratelencephalic-PNs (IT-PNs) from layer 5 of the motor cortex (M1). Comparatively, each IT-PN type has a distinct electrophysiological phenotype and the Fezf2+ve IT-PNs display a unique apical dendritic tuft. Here, we aimed to expand our understanding of the molecular underpinnings defining these unique IT-PN types. Using a validated Fezf2-GFP reporter mouse, retrograde labeling techniques and fluorescence activated cell sorting (FACS), combined with a novel approach for low-input RNA-sequencing, we isolated mature Fezf2+ve and Fezf2-ve IT-PNs for transcriptome profiling. Through the comparison of Fezf2+ve and Fezf2-ve IT-PN gene expression profiles, we identified significant enrichment of 81 genes in the Fezf2+ve IT-PNs and 119 genes in the Fezf2-ve IT-PNs. Term enrichment analysis of these enriched genes demonstrated significant overrepresentation of the calcium-binding EF-hand domain in Fezf2+ve IT-PNs, suggesting a greater importance for calcium handling in these neurons. Of the Fezf2-ve IT-PN enriched genes an unexpected and unique enrichment of genes, previously associated with microglia were identified. Our dataset identifies the molecular profiles of two unique IT-PN types in the mature M1, providing important targets to investigate for their maintenance in the healthy mature brain.

A comparative analysis of library prep approaches for sequencing low input translatome samples.

BACKGROUND: Cell type-specific ribosome-pulldown has become an increasingly popular method for analysis of gene expression. It allows for expression analysis from intact tissues and monitoring of protein synthesis in vivo. However, while its utility has been assessed, technical aspects related to sequencing of these samples, often starting with a smaller amount of RNA, have not been reported. In this study, we evaluated the performance of five library prep protocols for ribosome-associated mRNAs when only 250 pg-4 ng of total RNA are used. RESULTS: We obtained total and RiboTag-IP RNA, in three biological replicates. We compared 5 methods of library preparation for Illumina Next Generation sequencing: NuGEN Ovation RNA-Seq system V2 Kit, TaKaRa SMARTer Stranded Total RNA-Seq Kit, TaKaRa SMART-Seq v4 Ultra Low Input RNA Kit, Illumina TruSeq RNA Library Prep Kit v2 and NEBNext® Ultra™ Directional RNA Library Prep Kit using slightly modified protocols each with 4 ng of total RNA. An additional set of samples was processed using the TruSeq kit with 70 ng, as a 'gold standard' control and the SMART-Seq v4 with 250 pg of total RNA. TruSeq-processed samples had the best metrics overall, with similar results for the 4 ng and 70 ng samples. The results of the SMART-Seq v4 processed samples were similar to TruSeq (Spearman correlation > 0.8) despite using lower amount of input RNA. All RiboTag-IP samples had an increase in the intronic reads compared with the corresponding whole tissue, suggesting that the IP captures some immature mRNAs. The SMARTer-processed samples had a higher representation of ribosomal and non-coding RNAs leading to lower representation of protein coding mRNA. The enrichment or depletion of IP samples compared to corresponding input RNA was similar across all kits except for SMARTer kit. CONCLUSION: RiboTag-seq can be performed successfully with as little as 250 pg of total RNA when using the SMART-Seq v4 kit and 4 ng when using the modified protocols of other library preparation kits. The SMART-Seq v4 and TruSeq kits resulted in the highest quality libraries. RiboTag IP RNA contains some immature transcripts.

Preparation of Single-Cell RNA-Seq Libraries for Next Generation Sequencing.

For the past several decades, due to technical limitations, the field of transcriptomics has focused on population-level measurements that can mask significant differences between individual cells. With the advent of single-cell RNA-Seq, it is now possible to profile the responses of individual cells at unprecedented depth and thereby uncover, transcriptome-wide, the heterogeneity that exists within these populations. This unit describes a method that merges several important technologies to produce, in high-throughput, single-cell RNA-Seq libraries. Complementary DNA (cDNA) is made from full-length mRNA transcripts using a reverse transcriptase that has terminal transferase activity. This, when combined with a second "template-switch" primer, allows for cDNAs to be constructed that have two universal priming sequences. Following preamplification from these common sequences, Nextera XT is used to prepare a pool of 96 uniquely indexed samples ready for Illumina sequencing.