MERCURIUS™
Total DRUG-seq service
for high-throughput total RNA-seq
Our Total DRUG-seq service provides industry and academic scientists with ultra-scalable total RNA-seq, enabling whole-transcriptome screens of full-length coding and non-coding transcripts when isoform and alternative splicing detection is crucial. Ideal for large-scale compound discovery programs, next-generation toxicology strategies, and mechanism-of-action studies.
Discover Sample Report
Detects full-length transcripts
Bulk RNA sequencing at scale
No RNA extraction, no material loss
Ideal for large
RNA-seq projects
About the service
Our MERCURIUS™ Total DRUG-seq service is a convenient and scalable solution for projects of any size, enabling transcriptome-wide detection of full-length coding and non-coding transcripts.
As part of the Total DRUG-seq service, users simply deliver their 96- or 384-well plates containing frozen cells to us in Switzerland or the United States.
Upon receipt of the plates, our team prepares the libraries, then sequences to your desired read depth per sample, and then performs data pre-processing.
We return results, including raw fastq files, sequencing and alignment reports, and gene count matrices suitable for downstream differential expression analyses, once the data meet our rigorous quality control criteria.
During the process, we always keep clients informed at defined checkpoints so we can decide together how best to proceed to the next steps.
at 5M reads/sample
1. Client washes and freezes the cells
2. Client ships the plate(s) to Alithea
3. MERCURIUS™ Total DRUG-seq library prep
2 days
4. Library QC - Client Checkpoint
(Qubit, Fragment analyzer, shallow sequencing)
1 week
5. Deep Sequencing on AVITI or Illumina, depending on the request by the client
1 week
6. Data analysis and reporting- Client Checkpoint
1 week
7. Data Delivery
Raw FASTQ files, sample report file, QC files, and gene count tables
Total DRUG-seq has Uniform Gene Body Coverage Comparable to NEBNext® Ultra™ II Total RNA Prep
Gene body coverage profiles show the normalized 5’ to 3’ read density across transcript bodies for each library type in Huh7 cells. Total DRUG-seq displays uniform coverage across the full transcript length, matching NEBNext® Ultra™ II Total RNA preparations. Standard DRUG-seq shows characteristic 3’ enrichment, reflecting its 3’-end capture design.
Total DRUG-seq has High Gene and Transcript Detection Sensitivity Even at Low Read Depths
Gene-level (left) and transcript-level (right) detection as a function of sequencing depth after downsampling to between 0.1 and 7 million reads per sample. Both protocols detect comparable numbers of genes across all read depths. At the transcript level, Total DRUG-seq detects up to ~2.5-fold more transcripts than 3′ DRUG-seq at equivalent depths, reflecting its full-length coverage and ability to distinguish isoforms.
Total DRUG‑seq Reveals a Shift from Promoter 1 to Promoter 2 Isoforms of HNF4α in Response to TGFβ1
The HNF4α locus has differential isoform usage in response to increasing TGFβ1 dose. Total DRUG-seq (blue tracks) reveals a dose-dependent shift from Promoter 1-driven isoforms (blue box), which are progressively silenced upon TGFβ1 treatment, to Promoter 2-driven isoforms (red box), a hallmark of epithelial-to-mesenchymal transition (EMT) and tumor progression. This demonstrates Total DRUG-seq’s ability to detect biologically meaningful isoform switching events in drug response studies. 3′ DRUG-seq (green track) captures only the shared 3′-UTR region.
Sample Report
Fibroblasts
576
10M reads
15k
Publications
- 3' DRUG-seq, DRUG-seq
Rodrigues Sousa, E., de Brot, S., Zoni, E. et al.
- DRUG-seq, Spheroid DRUG-seq
Anstett, V.; Heinzelmann, E.; Piraino, F.; Roch, A.; Chrisnandy, A.; Norkin, M.; Garnier, V.; Homicsko, K.; Hoehnel-Ka, S.; Brandenberg, N.
What is the typical RNA input?
To generate high-quality sequencing data, we recommend starting with 5,000 to 25,000 mammalian cells per well for a 96-well plate and 2,000 to 10,000 cells per well for a 384-well plate.
The total RNA amount per pool should be at least 80,000 cells.
What are the validated cell lines?
What is the recommended sequencing depth for Total DRUG-seq?
Total DRUG-seq provides comprehensive coverage of full-length coding and non-coding transcripts.
We therefore recommend sequencing 10 to 20 million reads for each sample, depending on the project’s scale and scope.
What type of bioinformatic analysis is included in the service?
Ask your question:
Sample submission
Frozen cell submission guidelines
Lysate submission guidelines
Sample submission form
Validated Cell Lines
Liver / Hepatic Models
| HepaRG | Liver |
| HepG2 | Liver (HCC) |
| Huh7 | Liver (HCC) |
| Hep3B | Liver (HCC) |
| PHH (Primary Human Hepatocytes) | Primary liver |
| NCI-H295R | Adrenal/liver metabolism |
Cancer Cells
| MCF7 | Breast cancer |
| A549 | Lung carcinoma |
| H358 | Lung cancer |
| NCI-H1563 / H1048 | Lung cancer |
| DLD-1 | Colorectal cancer |
| SW837 | Colorectal cancer |
| HCT116 | Colorectal cancer |
| LS180 | Colorectal cancer |
| COLO201 | Colorectal cancer |
| C2BBe / C2BBe1 | Colorectal |
| GP5d | Colorectal |
| U2OS | Osteosarcoma |
| A172 | Glioblastoma |
| PSN-1 | Pancreatic cancer |
| AsPC-1 | Pancreatic cancer |
| SU.86.86 | Pancreatic cancer |
| A375 | Melanoma |
| HaCaT | Keratinocyte |
| UMUC3 | Bladder cancer |
| 5637 | Bladder cancer |
| HT1197 | Bladder cancer |
| Cal29 | Bladder cancer |
| UBLC1 | Bladder cancer |
Blood / Immune Cells
| PBMC | Primary blood |
| Jurkat | T-cell leukemia |
| Raji | B-cell lymphoma |
| THP-1 | Monocyte |
| U937 | Monocyte |
| MV-4-11 | AML |
| MOLM13 | AML |
| HL60 | Leukemia |
| MM1S | Myeloma |
| KMS12BM | Myeloma |
| CD4+ / CD8+ T cells | Primary immune |
| Tregs / TILs | Immune subsets |
| CD3 T cells | Immune |
Stem Cells / iPSC-Derived Models
| iPSC | Pluripotent |
| iPSC-derived neurons | Neural |
| iPSC-derived cardiomyocytes | Cardiac |
| iPSC-derived cortical neurons | Neural |
| iPSC-derived organoids | Various |
Neural / Glial Models
| HMC3 | Microglia |
| Luhmes | Dopaminergic neuron |
| IMR90 | Fibroblast (used in brain spheres) |
| iCell GlutaNeurons | Neurons |
| Astrocytes (human/mouse/rat) | Glial |
Fibroblast
| NHDF | Dermal fibroblast |
| MEF | Mouse embryonic fibroblasts |
Epithelial
| HEK293 / HEK293T | Kidney (transformed) |
| ARPE-19 | Retinal epithelium |
| NHEK | Keratinocytes |
| Lung epithelial | Epithelial |
| RPTEC/TERT1 | Kidney (proximal tubule) |
| T84 | Colon epithelium |
Adipose
| Human adipocytes | Primary |
| Brown adipocytes | Metabolic |
| Visceral adipocytes | Primary |
| Mouse/canine adipose | Animal |
Getting started with our MERCURIUS™ Total DRUG-seq services is simple
You can either book a call with our experts to discuss your project or submit your experimental details via our contact form so we can review your design and requirements.
Based on the goals, sample types, and scale of your study, we may recommend starting with a pilot project to optimise conditions and de-risk a larger screen.
If you’re interested in implementing the technology in your own lab instead, you can explore our MERCURIUS™ Total DRUG-seq kits on the dedicated kits page.
Related Blog Posts
August 25, 2026
Toxicology has traditionally relied on apical observations in animal models with limited use of high-throughput transcriptomics and AI. Yet these single endpoints with limited predictive…
June 29, 2026
For the first time, 1536-well whole transcriptome profiling brings biologically rich readouts to primary screening and large-scale perturbation dataset generation. In this webinar, Alithea Genomics…
March 25, 2026
Biomarker discovery using gene expression signatures has transformed pharmaceutical discovery and development, from early hit triage and target validation to toxicology, dose optimization, and patient…