Preclinical Cancer Transcriptome Profiling Powered by the HARCS Platform

Creative Biolabs provides end-to-end preclinical HARCS platform-based cancer transcriptome assays, covering project design, RNA extraction from challenging FFPE and low-input samples, strand-specific library preparation, high-depth sequencing, alignment and quantification, differential expression analysis, alternative splicing profiling, fusion-transcript detection, miRNA and long non-coding RNA characterization, and downstream pathway and biomarker interpretation. This solution is suited to researchers who need to compare tumor vs. normal transcriptomes, identify expression-based biomarkers, profile immune-related signatures, discover non-coding RNA biomarkers, validate splicing events, or screen for actionable fusions. Our team delivers sample and study strategy consultation, customized wet-lab and bioinformatics workflows, transparent QC checkpoints, structured data deliverables, and integrated preclinical study packages tailored to your tumor indication, RNA input quality, and translational goals.

Why the Transcriptome Carries the Most Direct Disease Signal

Downstream of the Genome, Upstream of the Phenotype

The transcriptome is the most direct downstream molecule of genetic variation and the most immediate upstream driver of cellular phenotype. For cancer biomarker discovery, transcript-level screening offers a decisive advantage over mutation-only profiling: loss-of-function mutations occur at relatively low frequency between cancer patients, so a single variant rarely serves as a robust predictive biomarker. In contrast, quantitative comparison of transcriptomes between samples exposes subtle shifts in gene expression, isoform usage, and non-coding RNA activity that collectively describe how a tumor is actually behaving in vivo and in vitro.

Why Screen the Cancer Transcriptome?
Most archived cancer specimens are FFPE-fixed, where DNA tends to survive but RNA is heavily fragmented and chemically modified. A platform that can rescue transcriptome information from such samples unlocks the largest existing archive of cancer tissue and makes retrospective biomarker studies possible.

Core Preclinical Challenges We Address:
Recovering usable sequencing data from FFPE samples with degraded and partially modified RNA.
Distinguishing tumor cell transcripts from stromal and immune-cell background in bulk data.
Capturing mRNA, miRNA, and long non-coding RNA in a coordinated profiling run.
Turning millions of expression measurements into a small set of biologically interpretable biomarkers.

How HARCS Cancer Transcriptome Differs from Generic RNA-seq?

Key Comparison	Generic RNA-seq Workflow	HARCS Cancer Transcriptome Assay
FFPE Sample Compatibility	Variable; often needs re-extraction or rRNA depletion tweaks.	Optimized chemistry for fragmented FFPE RNA with built-in quality gating.
Coverage & Resolution	Standard gene-level counts; isoform signal often lost.	Deep coverage across mRNA, miRNA, and lncRNA with isoform-level resolution.
Fusion & Splicing Detection	Requires extra dedicated assays or pipelines.	Built-in fusion transcript and alternative splicing profiling from the same library.
Biomarker-Ready Output	Raw counts; interpretation left to the client.	Pathway, immune-signature, and candidate biomarker tables delivered with every project.

End-to-End Cancer Transcriptome Service Packages

Our preclinical cancer transcriptome services are structured into flexible, modular packages. We understand that every project is unique; therefore, all modules can be fully customized—from RNA input requirements to bioinformatics depth—to align with your tumor indication, sample type, and downstream translational goals.

Strategy

Project Design & Sample Strategy

Strategic planning and optimized sample processing to ensure high-quality starting material for transcriptome profiling.

Indication Evaluation: Tumor indication review and feasibility based on sample availability.
Customized Sampling: Processing protocols for FFPE, fresh-frozen tissue, plasma exosomes, and sorted cell populations.
RNA QC Gating: DV200, RIN, and yield thresholds defined up-front for every sample class.
Customized Path: Tailored timelines, sequencing depth targets, and risk mitigation strategies.

Library

RNA Extraction & Library Preparation

High-recovery RNA purification and strand-specific library construction adapted to each sample type.

Total RNA Recovery: Optimized extraction from low-input and FFPE-derived material.
rRNA Depletion: Ribosomal RNA removal with broad species coverage for comprehensive profiling.
Strand-Specific Libraries: Retain transcript orientation for accurate antisense and overlapping gene analysis.
Quality Controls: Pre- and post-library Bioanalyzer traces and qPCR-based yield checks.

Sequencing

High-Depth Sequencing on HARCS

Deep, balanced sequencing runs optimized for differential expression and isoform detection.

Depth Targeting: Configurable read depth (commonly 30M–100M paired reads) per sample.
Read Length: Optimized for confident alignment across splice junctions.
Batch Balancing: Multiplexing schemes designed to minimize lane and index bias.
Run QC: Per-lane quality metrics with go/no-go checkpoints before data delivery.

Analysis

Alignment, Quantification & DGE

Robust computational pipeline producing gene- and transcript-level measurements ready for biological interpretation.

Splice-Aware Alignment: Reference-based alignment optimized for vertebrate and cancer model genomes.
Quantification: Gene- and isoform-level counts with normalization across conditions.
Differential Expression: Statistical ranking of up- and down-regulated genes with multiple-testing correction.
Reproducibility: Containerized pipelines with version-pinned tools and traceable parameters.

Insight

Isoform, Fusion & Non-Coding RNA

Extended analyses that go beyond standard gene-level differential expression to capture cancer-specific RNA biology.

Alternative Splicing: Isoform usage changes and event-level quantification across conditions.
Fusion Transcripts: Candidate gene fusions with annotation and confidence scoring.
miRNA & lncRNA: Coordinated small-RNA and long non-coding RNA profiling and differential analysis.
Immune Signatures: Tumor microenvironment and immune-cell deconvolution scores per sample.

Deliverables

Interpretation & Reporting

Structured data packages and biological interpretation designed for translational research workflows.

Pathway Analysis: Enrichment across curated cancer and immune-relevant pathways.
Candidate Biomarkers: Ranked shortlist of expression-based biomarkers with supporting evidence.
QC Report: Sample-, library-, and run-level quality summary with acceptance criteria.
Data Package: Raw FASTQ, processed counts, tables, and an analysis notebook ready for downstream use.

Optimized Preclinical Cancer Transcriptome Profiling Workflow

Integrated transcriptome profiling workflow

Phase 1 — Sample Intake & RNA Quality Gating

We accept FFPE, fresh-frozen tissue, plasma-derived RNA, and sorted cell populations. Each sample is logged, sectioned or aliquoted under standardized conditions, and gated on RNA integrity (e.g., DV200 for FFPE) before library construction. Samples that fall below the agreed threshold trigger a client-facing recommendation rather than silent failure.

Phase 2 — Strand-Specific Library Construction

Total RNA undergoes ribosomal RNA depletion and fragmentation control matched to each input quality. Strand-specific libraries are constructed to preserve transcript orientation, enabling accurate quantification of antisense transcripts and overlapping gene pairs. Pre-library and post-library QC traces are captured for the run record.

Phase 3 — High-Depth HARCS Sequencing

Barcoded libraries are pooled and sequenced on the HARCS platform at a depth matched to the experimental design (commonly 30M–100M paired reads per sample). Read length and lane distribution are configured to give confident coverage across splice junctions and lowly expressed transcripts.

Phase 4 — Alignment, Quantification & DGE

Reads are aligned with splice-aware tools, deduplicated, and quantified at gene and isoform level. Differential expression is computed with a workflow that supports multi-group designs, batch covariates, and multiple-testing correction. Fusion and alternative splicing events are flagged as a separate analysis layer on the same dataset.

Phase 5 — Pathway, Immune & Biomarker Insight

Final outputs include pathway and gene-set enrichment, tumor microenvironment and immune-signature scores, and a curated shortlist of candidate biomarkers ranked by statistical and biological evidence. Results are delivered together with raw and processed data so that they can be carried forward into downstream preclinical validation.

Enabling Technologies Behind the HARCS Cancer Transcriptome Assay

FFPE-Adapted RNA Recovery

A dedicated extraction and quality-gating workflow for archived FFPE samples. Fragmented and chemically modified RNA is recovered with controlled bias, allowing retrospective and prospective cancer cohorts to be profiled on the same platform.

Strand-Specific Library Chemistry

Directional library preparation that preserves transcript strand information. This is essential for resolving overlapping gene pairs, antisense transcription, and accurately attributing reads to the correct genomic feature.

Splice-Aware Quantification & Isoform Analysis

Alignment and quantification pipelines tuned for splice-junction reads and isoform-level expression. Differential isoform usage and alternative splicing events can be reported alongside gene-level statistics from the same dataset.

Why Choose Creative Biolabs?

Deep Transcriptome Expertise

Years of experience in cancer RNA biology, with scientists who understand how RNA quality, library chemistry, and depth choices interact to shape the final answer.

FFPE-Friendly Workflow

A platform and protocol stack that opens up archived FFPE material, the largest and most clinically annotated source of human cancer tissue available.

Customizable Service Modules

From sequencing depth to bioinformatics focus, every step can be tailored to your tumor indication, sample type, and translational question.

End-to-End Reliability

Transparent QC checkpoints, version-pinned pipelines, and structured data deliverables that are ready to hand off to downstream preclinical teams.

Research Insight: Transcriptome-Based Cancer Biomarker Discovery

Key Findings from Preclinical & Comparative Studies

Quantitative transcriptome comparison has become a central strategy for finding cancer biomarkers that are robust across patient cohorts. The findings below summarize the patterns that make transcript-level screening attractive for preclinical cancer research.

FFPE Is No Longer a Hard Limit: Recent comparative studies show that, with appropriate library chemistry and depth, transcriptome profiling on small FFPE samples can approach the data quality obtained from fresh-frozen material, opening the door to large retrospective cancer cohorts.
High-Throughput Profiling Drives Biomarker Yield: When sequencing depth and replication are matched to the biological question, RNA-based screening consistently surfaces more candidate biomarkers per project than mutation-only panels, while keeping cost and turnaround compatible with preclinical timelines.
Beyond Gene Counts: Isoform usage, fusion transcripts, and non-coding RNA layers each carry independent biomarker information. Coordinated profiling of these layers from a single dataset multiplies the chance of finding a clinically meaningful candidate.

Schematic of a system-biology strategy for natural drug discovery based on transcriptome sequencing.

Fig.1 System-biology strategy for natural drug discovery based on transcriptome sequencing.^1,2

FAQs Regarding HARCS Cancer Transcriptome Assays

We routinely accept FFPE, fresh-frozen tissue, plasma-derived RNA, and sorted cell populations. For FFPE samples we use DV200 as the primary quality gate, while fresh-frozen samples are evaluated by RIN and yield. Every batch is gated up-front, and samples that fall below threshold trigger a recommendation rather than silent failure.

Yes. By using rRNA depletion rather than poly(A) selection, the same library retains mRNA, long non-coding RNA, and many small RNA species. For projects that require dedicated miRNA quantification, a small-RNA library preparation can be added as a parallel module.

Bulk RNA-seq reflects the average transcriptome of all cells in the input. We pair standard gene-level analysis with immune and stromal deconvolution scores so that shifts in the tumor microenvironment can be distinguished from tumor-intrinsic expression changes in vivo and in vitro.

Standard deliverables include raw FASTQ files, processed gene- and isoform-level count tables, a differential expression worksheet, pathway and immune-signature enrichment tables, a candidate biomarker shortlist, and an end-to-end QC report. Optional deliverables include fusion calls, alternative splicing events, and a reproducible analysis notebook.

Yes. Reference databases, pathway gene sets, and deconvolution matrices can be tailored to your indication, such as breast, lung, colorectal, hematological, or central nervous system tumors. The analysis plan is finalized at project kick-off so that the deliverables match your downstream translational workflow.