転写物がセンス・アンチセンス鎖のどちらのDNA鎖から転写されるかが区別されます。遺伝子発現をより正しく把握するため、ジーンウィズではこれを標準仕様にしております。mRNAを対象にしたpoly-A選択法(デフォルト)のほか、ノンコ―ディングRNAを含む全遺伝子の検出(small RNAを除く)や分解が進んでいるRNAを対象にしたrRNA枯渇法のオプションをご提供しております。
1,000~10,000細胞のオーダーで、細胞個々を区別して遺伝子発現解析を行います。標準的なRNA-Seqが全細胞集団にわたる平均的な遺伝子の発現状態を検出するのに対し、シングルセルRNA-Seqでは、細胞集団の不均一性や希少細胞の同定が可能になります。 関連するアプリケーションとして、同一細胞でTCR/BCRの可変領域同定と遺伝子発現解析を行うシングルセルレパトア解析、オープンクロマチンの性状を同時に解析するマルチオームATAC+発現解析などがあります。
NGS プラットフォームおよびプロジェクトの推奨構成については、NGS プラットフォームのページ をご覧ください。 Azenta は、シーケンスのみのプロジェクトのデータ出力や品質を保証しません。.
Poly(A) tail sequences can impact the integrity of your mRNA plasmids for in vitro transcription. While longer poly(A) tails have been shown to increase their stability, tails greater than 100 bases are susceptible to truncations. Learn how the proprietary mRNA plasmid preparation protocol from Azenta can help you generate higher yields and preserve poly(A) tails of greater lengths compared to standard protocols for high-fidelity templates in our tech note.
Strict quality control is required to maintain the integrity of manufactured products for RNA therapies, but current assays often present limitations. Learn how the novel full-length RNA-Seq approach from Azenta allows you to preserve the entire length of and effectively sequence the poly(A) tails of your mRNA products.
For those new to bioinformatics, analyzing massive amounts of NGS data can be a daunting task. Download Azenta’s bioinformatics quick start guide to learn how to analyze whole genome sequencing (WGS) and RNA sequencing (RNA-Seq) data with bioinformatics tools to reveal biological insights for your research.
Developing immunotherapies for cancer can be difficult due to the variation of immune response from patient to patient. In this webinar, Dr. Litchfield from UCL Cancer Institute presents his team’s exploratory research using a multiomics approach to better understand the diversity of immune response to cancer and highlights their findings of an alternative source of a tumour-specific antigen in checkpoint inhibitor (CPI) response.
With this two-part webinar series, go beyond traditional transcriptomics and learn about the various NGS approaches available for gene expression analysis. In part 1, we take an in-depth look at various gene expression approaches, including RNA-Seq, single-cell RNA-Seq, digital spatial profiling, and more. In part 2, we explore the data generated from these approaches and how they can complement each other and confirm findings.
RNA-Seq bioinformatics can be complex and difficult to decipher. To help make it more approachable, this workshop and roundtable discussion, led by Azenta Life Sciences' bioinformatics manager Brian Sereni, explores the bioinformatics pipeline, explains NGS results, and addresses common challenges and FAQs for RNA-Seq bioinformatics analysis.
Obtaining samples with high cell viability can be difficult for many experiments but is necessary for success on the 10x Genomics® Chromium™ platform. This tech note describes how Azenta scientists used optimized single-cell workflows, including dead cell removal, to overcome low viability and generate high-quality sequencing data.
Biomedical specimens are often restricted to minute quantities, posing major limitations to RNA-Seq. This case study shows how approximately 50 sorted cells from a glioblastoma can produce transcriptomic data comparable to RNA-Seq experiments that use millions of cells.
Cell populations are rarely homogeneous and synchronized in their characteristics. Standard RNA-Seq approaches are limited to reporting general expression levels thus omitting minor subpopulation profiles. This study highlights new single-cell RNA sequencing capabilities for identifying rare cells, characterizing their transcriptomes, and discovering potential biomarkers.
Contiguous mRNA full-length sequencing (Iso-Seq) greatly simplifies genome annotation efforts and revolutionizes the discovery of novel RNA isoforms. This Tech Note discusses the advantages of the latest technologies combined with Azenta’s optimized workflow, and how this increases output and accuracy.
In comparison to traditional profiling methods which assess bulk populations, single-cell technologies empower researchers to examine diversity of heterogeneous cell populations and uncover new, and potentially unexpected, biological discoveries. This webinar highlights the unparalleled capabilities of single-cell sequencing.
High-throughput technologies are critical in performing phenotypic profiling for drug discovery applications. In this tech note, Azenta Life Sciences discusses the challenges associated with traditional approaches, such as microarrays and RNA sequencing, and offers an optimized assay to achieve high-quality phenotypic profiling at a reduced cost for rapid drug discovery.
This selection guide offers practical information about PCR + Sanger, qPCR, and NGS approaches to help you determine which assay best suits your project requirements, along with an interactive assay selection tool to aid your decision making.
The structure of mRNA has been known for decades, but only in recent years have researchers unlocked its potential for therapeutic development. With the right delivery vehicle, mRNA products can replace defective proteins in the cell, generate antigens for immunization (e.g., COVID vaccines), or edit the genome via CRISPR technology. Let’s review the structural features of a functional mRNA molecule and discuss how to optimize these for therapeutic applications.
RNAシーケンシングまたはRNA-Seqは、サンプル中のRNA鎖の配列決定し定量する目的で利用されます。カウント データを生成することにより、細胞のトランスクリプトームについてより深い洞察を提供し、新規転写物の発見と差次的な遺伝子発現解析を可能にします。カウントデータを分析する際、研究者は RNA 断片を定量化し、RNA 配列をサンプル内のそれぞれの遺伝子に関連付けることができます。
RNA-Seq のプロセスは、RNA 鎖を単離して断片化することから始まり、次に相補的 DNA (cDNA) の合成、つまり逆転写 を経て、次世代シーケンシング (NGS) テクノロジーを使用して RNA 鎖を配列決定します。
RNA シーケンス (RNA-Seq) 解析は、シーケンスリードを参照ゲノムまたはde novoアセンブリデータにマッピングし、個々の遺伝子と転写物の発現レベルを定量化し、サンプル間で差次的に発現する標的遺伝子と転写物を特定することによって解析します。低濃度および/または低品質のサンプル溶液を使用して、新規トランスクリプトームアセンブリを実行することもできます。
NGS プラットフォームおよびプロジェクトの推奨構成については、NGS プラットフォームのページ をご覧ください。 Azenta は、シーケンスのみのプロジェクトのデータ出力や品質を保証しません。.