Fix your cells the moment you collect them.
Evercode is the single cell workflow built for real cancer samples. Fix cells at collection, process them on your timeline, and batch across timepoints. No dedicated instrument standing between your samples and your data.
You don't control when samples arrive, but droplet workflows make you process them fresh, on the spot. So a cohort ends up split across separate runs, and every split adds a batch effect to correct for later.
Fix at collection, then process when you're ready. Pool every sample across days, sites, and timepoints into a single run.
Your samples shouldn't wait on an instrument
If you already run single cell, you know the workflow tax. Samples have to be fresh and processed on the instrument's schedule, cohorts get split across runs, and every split adds batch effects you spend analysis time correcting.
Fresh-only pressure
Precious tumor samples arrive on their own schedule, but a droplet workflow needs them processed fresh. That means racing to dissociate, get to the core, and book instrument time before the sample degrades.
Cohorts split across runs
Large studies can't fit one run, so samples get batched separately, introducing technical variation that confounds real biology.
Logistics dictate design
Multi-site and longitudinal cancer studies bend to the platform's constraints instead of the science's.
Design the experiment you want. Not the one the instrument allows.
Because Evercode fixes cells at collection and needs no dedicated instrument, three constraints you've worked around simply go away.
More sample types, without giving up the whole transcriptome.
Fixation at collection is the core of the Evercode workflow. It opens up sample types and study designs that fresh, instrument-bound workflows can't easily accommodate. And it does so without locking you into a predefined gene panel.
- Profile both cells and nuclei from fixed samples.
- Access archival tissue, including FFPE, to mine biobanks with linked clinical outcomes.
- Read the entire transcriptome, not a fixed probe set, so you are not choosing your genes before you see the data.
- Decouple collection from processing. Fix now, process later, across sites and timepoints.
Run the whole cohort together.
There's no instrument partitioning cells one droplet at a time, so you can pool many samples into a single run. How big your cohort gets is a question of study design, not how many chips your instrument can process.
- Process many samples in one experiment and keep large studies on a single, consistent workflow.
- Cut batch effects by pooling samples into shared runs instead of splitting them across instrument sessions.
- Scale cost-efficiently, because combinatorial barcoding grows with samples, not with per-cell partitioning hardware.
Whole-transcriptome depth, plus paired immune profiling on fixed cells.
Evercode reads the full transcript, not a fixed probe panel, so you keep the discovery range cancer biology demands. And you can add TCR or BCR immune profiling within the same fixation-based workflow.
- Add paired TCR or BCR profiling to fixed samples for immuno-oncology work, including checkpoint, CAR-T, and adoptive-therapy studies.
- Detect isoforms, splicing, and lncRNA across the whole transcriptome, without pre-selecting targets.
- Profile across species without protocol changes, ideal for PDX, xenograft, and model comparisons.
- Run discovery and immune-repertoire questions through one workflow, on the same samples.
Sensitivity that speaks for itself.
In head-to-head comparisons against droplet and probe-based platforms, Evercode captures more genes per cell across more RNA biotypes, with clean, low-background data.
The workflow, run on real tumors.
Cancer datasets generated on Evercode, spanning archival tissue, dissociated tumors, and head-to-head platform comparisons.
Breast cancer, straight from the block
Whole-transcriptome profiling of four archival FFPE samples resolved tumor, stromal, and immune compartments and distinguished ER+, HER2+, and TNBC subtypes.
Kidney tumors, pooled and resolved
Fifteen renal cell carcinoma samples across three subtypes plus normal adjacent tissue, fixed and processed together, resolving subtype-specific cell populations from ~31,600 cells.
Mapping GBM heterogeneity
Nuclei from multiple anatomical regions of patient glioblastomas, capturing regionally distinct malignant and immune cell states across the tumor microenvironment.
Tracking T cells as cancer spreads
The Reticker-Flynn Lab at Stanford paired whole-transcriptome and TCR sequencing to map how T cell clonotypes in tumor-draining lymph nodes shift during metastasis.
"A powerful assay to explore complex tumor microenvironments."
Design your study around your science, not a box.
Tell us what you're trying to profile and how your study is structured. A Parse scientist will walk you through whether the Evercode workflow fits. No obligation.