Primary Cell Rna
Primary Cell Rna - Related Videos
10:15
Small RNA Transfection in Primary Human Th17 Cells by Next Generation Electroporation
0 Views •
Cited by 5 •
2017
Next generation electroporation is an efficient method for transfecting human Th17 cells with small RNAs to alter gene expression and cell behavior.
17:25
RNA Extraction from Neuroprecursor Cells Using the Bio-Rad Total RNA Kit
0 Views •
Cited by 1 •
2007
10:36
Genome Editing in Primary Mammalian Cells via Electroporation of Editor RNA
0 Views •
2026
The delivery of genome editors as RNAs is a particularly potent means of modifying the genomes of primary mammalian cells and cell lines. The goal of this protocol is to deliver genome editors as mRNA into primary mammalian cells and cell lines, followed by targeted Illumina sequencing and analysis to quantify editing outcomes.
11:38
RNA-based Reprogramming of Human Primary Fibroblasts into Induced Pluripotent Stem Cells
0 Views •
Cited by 19 •
2018
Here we describe a clinically relevant, high-efficiency, feeder-free method to reprogram human primary fibroblasts into induced pluripotent stem cells using modified mRNAs encoding reprogramming factors and mature microRNA-367/302 mimics. Also included are methods to assess reprogramming efficiency, expand clonal iPSC colonies, and confirm expression of the pluripotency marker TRA-1-60.
11:26
Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells
0 Views •
Cited by 8 •
2017
Here, we present a combinatorial approach for classifying neuronal cell types prior to isolation and for the subsequent characterization of single-cell transcriptomes. This protocol optimizes the preparation of samples for successful RNA Sequencing (RNA-Seq) and describes a methodology designed specifically for the enhanced understanding of cellular diversity.
FAQs
RNA can lose its value as a biological snapshot if cells remain untreated during handling. Stabilization or rapid processing helps preserve the gene activity state present when the tissue or patient sample was obtained. This is especially important in cancer research, where handling-related changes could be mistaken for tumor-associated expression programs or treatment responses.
RNases are enzymes that can degrade RNA during cell lysis and purification. Controlling RNase exposure helps maintain RNA suitable for quality assessment and downstream analysis. If degradation occurs, the resulting material may provide a less reliable representation of cellular gene activity, weakening comparisons between malignant and nonmalignant populations or between treated and untreated samples.
Because the cells originate directly from tissues or patient samples, their RNA can retain features of the original biological state. This connection to patient biology supports investigation of expression differences that may be difficult to capture in more removed experimental systems. In cancer studies, the material can therefore help relate molecular patterns to malignant and nonmalignant populations.
After purification and quality assessment, the RNA can support reverse transcription or sequencing. Reverse transcription converts RNA information into a form suitable for subsequent analysis, while sequencing can characterize gene activity patterns more broadly. These approaches allow investigators to examine tumor-associated expression programs, compare cell populations, and evaluate molecular changes associated with cancer treatment.
The workflow begins with cells obtained directly from the relevant tissue or patient sample. Researchers then stabilize or rapidly process the material, lyse the cells under RNase-controlled conditions, and purify the RNA. Quality assessment follows before reverse transcription or sequencing. Maintaining this sequence connects sample handling with the reliability of the final molecular measurements.
Cancer researchers can examine primary cell RNA to identify tumor-associated expression programs, measure molecular responses to treatment, and distinguish malignant from nonmalignant populations. These results can contribute to biomarker discovery and disease modeling. They also support more representative evaluation of cancer therapies by preserving a closer relationship to patient biology than an abstract molecular readout alone.