Application of single-cell sequencing technologies in pancreatic cancer

Single-cell transcriptome analysis for cancer and biology of the pancreas: A review on recent progress

Single-cell sequencing has become one of the most used techniques across the wide field of biology. It has enabled researchers to investigate the whole transcriptome at the cellular level across tissues, which unlocks numerous potentials for basic and applied studies in future diagnosis and therapy. Here, we review the impact of single-cell RNA sequencing, as the prominent single-cell technique, in pancreatic biology and cancer. We discuss the most recent findings about pancreatic physiology and pathophysiology owing to this technological advancement in the past few years. Using single-cell RNA sequencing, researchers have been able to discover cellular heterogeneity across healthy cell types, as well as cancer tissues of the pancreas. We will discuss the new immunological targets and new molecular mechanisms of progression in the microenvironment of pancreatic cancer studied using single-cell RNA sequencing. The scope is not limited to cancer tissues, and we cover novel developmental, evolutionary, physiological, and heterogenic insights that have also been achieved recently for pancreatic tissues. We cover all biological insights derived from the single-cell RNA sequencing data, discuss the corresponding pros and cons, and finally, conclude how future research can move better by utilizing single-cell analysis for pancreatic biology.

Cancer-associated fibroblasts in acute leukemia

Although the prognosis for acute leukemia has greatly improved, treatment of relapsed/refractory acute leukemia (R/R AL) remains challenging. Recently, increasing evidence indicates that the bone marrow microenvironment (BMM) plays a crucial role in leukemogenesis and therapeutic resistance; therefore, BMM-targeted strategies should be a potent protocol for treating R/R AL. The targeting of cancer-associated fibroblasts (CAFs) in solid tumors has received much attention and has achieved some progress, as CAFs might act as an organizer in the tumor microenvironment. Additionally, over the last 10 years, attention has been drawn to the role of CAFs in the BMM. In spite of certain successes in preclinical and clinical studies, the heterogeneity and plasticity of CAFs mean targeting them is a big challenge. Herein, we review the heterogeneity and roles of CAFs in the BMM and highlight the challenges and opportunities associated with acute leukemia therapies that involve the targeting of CAFs.

Laser Capture Microdissection: A Gear for Pancreatic Cancer Research

The advancement in molecular techniques has been attributed to the quality and significance of cancer research. Pancreatic cancer (PC) is one of the rare cancers with aggressive behavior and a high mortality rate. The asymptomatic nature of the disease until its advanced stage has resulted in late diagnosis as well as poor prognosis. The heterogeneous character of PC has complicated cancer development and progression studies. The analysis of bulk tissues of the disease was insufficient to understand the disease, hence, the introduction of the single-cell separating technique aided researchers to decipher more about the specific cell population of tumors. This review gives an overview of the Laser Capture Microdissection (LCM) technique, one of the single-cell separation methods used in PC research.

Prostate cancer in omics era

Recent advances in omics technology have prompted extraordinary attempts to define the molecular changes underlying the onset and progression of a variety of complex human diseases, including cancer. Since the advent of sequencing technology, cancer biology has become increasingly reliant on the generation and integration of data generated at these levels. The availability of multi-omic data has transformed medicine and biology by enabling integrated systems-level approaches. Multivariate signatures are expected to play a role in cancer detection, screening, patient classification, assessment of treatment response, and biomarker identification. This review reports current findings and highlights a number of studies that are both novel and groundbreaking in their application of multi Omics to prostate cancer.

Integrative analysis of transcriptome complexity in pig granulosa cells by long-read isoform sequencing

Background

In intensive and large-scale farms, abnormal estradiol levels in sows can cause reproductive disorders. The high incidence rate of reproductive disturbance will induce the elimination of productive sows in large quantities, and the poor management will bring great losses to the pig farms. The change in estradiol level has an important effect on follicular development and estrus of sows. To solve this practical problem and improve the productive capacity of sows, it is significant to further clarify the regulatory mechanism of estradiol synthesis in porcine granulosa cells (GCs). The most important function of granulosa cells is to synthesize estradiol. Thus, the studies about the complex transcriptome in porcine GCs are significant. As for precursor-messenger RNAs (pre-mRNAs), their post-transcriptional modification, such as alternative polyadenylation (APA) and alternative splicing (AS), together with long non-coding RNAs (lncRNAs), may regulate the functions of granulosa cells. However, the above modification events and their function are unclear within pig granulosa cells.

Methods

Combined PacBio long-read isoform sequencing (Iso-Seq) was conducted in this work for generating porcine granulosa cells' transcriptomic data. We discovered new transcripts and possible gene loci via comparison against reference genome. Later, combined Iso-Seq data were adopted to uncover those post-transcriptional modifications such as APA or AS, together with lncRNA within porcine granulosa cells. For confirming that the Iso-Seq data were reliable, we chose four AS genes and analyzed them through RT-PCR.

Results

The present article illustrated that pig GCs had a complex transcriptome, which gave rise to 8,793 APA, 3,465 AS events, 703 candidate new gene loci, as well as 92 lncRNAs. The results of this study revealed the complex transcriptome in pig GCs. It provided a basis for the interpretation of the molecular mechanism in GCs.

Deep learning tackles single-cell analysis-a survey of deep learning for scRNA-seq analysis

Since its selection as the method of the year in 2013, single-cell technologies have become mature enough to provide answers to complex research questions. With the growth of single-cell profiling technologies, there has also been a significant increase in data collected from single-cell profilings, resulting in computational challenges to process these massive and complicated datasets. To address these challenges, deep learning (DL) is positioned as a competitive alternative for single-cell analyses besides the traditional machine learning approaches. Here, we survey a total of 25 DL algorithms and their applicability for a specific step in the single cell RNA-seq processing pipeline. Specifically, we establish a unified mathematical representation of variational autoencoder, autoencoder, generative adversarial network and supervised DL models, compare the training strategies and loss functions for these models, and relate the loss functions of these models to specific objectives of the data processing step. Such a presentation will allow readers to choose suitable algorithms for their particular objective at each step in the pipeline. We envision that this survey will serve as an important information portal for learning the application of DL for scRNA-seq analysis and inspire innovative uses of DL to address a broader range of new challenges in emerging multi-omics and spatial single-cell sequencing.

Third-Generation Sequencing: The Spearhead towards the Radical Transformation of Modern Genomics

Although next-generation sequencing (NGS) technology revolutionized sequencing, offering a tremendous sequencing capacity with groundbreaking depth and accuracy, it continues to demonstrate serious limitations. In the early 2010s, the introduction of a novel set of sequencing methodologies, presented by two platforms, Pacific Biosciences (PacBio) and Oxford Nanopore Sequencing (ONT), gave birth to third-generation sequencing (TGS). The innovative long-read technologies turn genome sequencing into an ease-of-handle procedure by greatly reducing the average time of library construction workflows and simplifying the process of de novo genome assembly due to the generation of long reads. Long sequencing reads produced by both TGS methodologies have already facilitated the decipherment of transcriptional profiling since they enable the identification of full-length transcripts without the need for assembly or the use of sophisticated bioinformatics tools. Long-read technologies have also provided new insights into the field of epitranscriptomics, by allowing the direct detection of RNA modifications on native RNA molecules. This review highlights the advantageous features of the newly introduced TGS technologies, discusses their limitations and provides an in-depth comparison regarding their scientific background and available protocols as well as their potential utility in research and clinical applications.

The Single-Cell Sequencing: A Dazzling Light Shining on the Dark Corner of Cancer

Tumorigenesis refers to the process of clonal dysplasia that occurs due to the collapse of normal growth regulation in cells caused by the action of various carcinogenic factors. These “successful” tumor cells pass on the genetic templates to their generations in evolutionary terms, but they also constantly adapt to ever-changing host environments. A unique peculiarity known as intratumor heterogeneity (ITH) is extensively involved in tumor development, metastasis, chemoresistance, and immune escape. An understanding of ITH is urgently required to identify the diversity and complexity of the tumor microenvironment (TME), but achieving this understanding has been a challenge. Single-cell sequencing (SCS) is a powerful tool that can gauge the distribution of genomic sequences in a single cell and the genetic variability among tumor cells, which can improve the understanding of ITH. SCS provides fundamental ideas about existing diversity in specific TMEs, thus improving cancer diagnosis and prognosis prediction, as well as improving the monitoring of therapeutic response. Herein, we will discuss advances in SCS and review SCS application in tumors based on current evidence.