News Archives - Decode Science

Whole transcriptome single cell analysis for FFPE tissues

April 1, 2026/in News, Parse/by Harshita Sharma

Whole transcriptome single cell analysis for FFPE tissues

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Single Cell Whole Transcriptome Analysis of Breast Cancer FFPE Samples Across Tumor Subtypes

Key Takeaways

Whole transcriptome single cell profiling from archival FFPE breast cancer samples resolves tumor, stromal, and immune compartments in a single experiment
Distinct epithelial programs are identified across clinically relevant subtypes, including ER+, ER/PR+, HER2+, and TNBC
Subtype- and proliferation-associated lncRNA expression patterns are captured, highlighting the value of unbiased RNA profiling in FFPE samples

Experimental Design:

Formalin-fixed, paraffin-embedded (FFPE) tissue represents a vast source of clinically annotated samples, but has been difficult to use for whole transcriptome single cell analysis. In this dataset, nuclei isolated from 4 archived breast cancer FFPE samples were profiled using Evercode WT FFPE’s reverse transcription–based workflow designed to capture whole transcriptome expression from degraded RNA, profiling over 100,000 nuclei.

These results demonstrate that FFPE samples enable whole transcriptome profiling that captures meaningful cell types and tumor subtype biology across multiple donors while preserving cellular heterogeneity.

Results:

Whole transcriptome profiling resolved epithelial tumor populations alongside stromal and immune compartments, including CAFs, endothelial cells, VSMCs, myeloid cells, pDCs, B cells, NKT cells, and mast cells.

Cells cluster strongly by donor, with clear differences in gene expression programs across samples. Subtype-specific expression patterns distinguish ER+, ER/PR+, HER2+, and TNBC tumors, with TNBC remaining particularly distinct after integration.

Figure 1: UMAP of human breast cancer FFPE nuclei. Tumor and proliferative epithelial states are resolved together with stromal and immune populations from FFPE samples.

Subtype- and state-associated lncRNA expression

Whole transcriptome profiling enables detection of biologically relevant lncRNAs across breast tumor cell states, revealing patterns linked to both tumor subtype and functional cell state.

LINC00993, a lncRNA associated with tumor-suppressive activity in breast cancer, is enriched in luminal epithelial populations. In contrast, the oncogenic lncRNA TUG1 shows higher expression in TNBC and proliferating epithelial states. These patterns are consistent with known subtype-associated biology and highlight how lncRNA expression reflects underlying tumor programs.

Figure 3: Proliferation-associated lncRNA expression.

Expression of TUG1 and additional lncRNAs across proliferating epithelial populations marked by Ki67 protein expression. Proliferating tumor cells show increased expression of specific lncRNAs, linking noncoding RNA activity to cell cycle state.

Proliferating epithelial populations show coordinated expression of TUG1 alongside proliferation markers, indicating an association between lncRNA activity and cell cycle progression.

Together, these results demonstrate that whole transcriptome FFPE profiling captures both coding and noncoding features of tumor biology across subtype and cell state.

Figure 2: Subtype-associated lncRNA expression across breast tumor cell states.

To further connect lncRNA expression with functional and clinical measures of proliferation, lncRNA expression was assessed using Ki67 positivity, as determined by prior immunohistochemistry protein staining.

Dot plot showing expression of LINC00993 and TUG1 across annotated cell populations. LINC00993 is enriched in luminal epithelial populations, while TUG1 is elevated in TNBC and proliferating epithelial populations. Dot size indicates the percent of cells expressing each transcript and color indicates average expression.

As the official distributor in Australia and New Zealand, Decode Science makes accessing genomics solutions straightforward. Our role is to connect your lab with advanced technologies, ensuring you get the right solution for your sequencing projects—delivered locally with support when you need it.

Proteomics in Transition: From Discovery to Diagnostic Relevance Whitepaper

November 24, 2025/in News, Quanterix/by Harshita Sharma

Proteomics in Transition: From Discovery to Diagnostic Relevance Whitepaper

For years, discovery proteomics uncovered hundreds of candidate biomarkers — but most stalled before reaching the clinic. New digital platforms change that: they deliver femtogram-level sensitivity, reproducible quantification across sites, and the analytical rigor required for regulatory and clinical use.

Why this matters
Proteins reflect real-time biology. That means faster detection, better trial enrollment, and clearer measures of therapeutic effect. Whether you’re developing a diagnostic, designing an adaptive trial, or building a multi-omics model, the right proteomic data reduces guesswork and accelerates decisions.

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BCR Sequencing of 1 Million Healthy and Diseased Samples in a Single Experiment

November 24, 2025/in News, Parse/by Harshita Sharma

BCR Sequencing of 1 Million Healthy and Diseased Samples in a Single Experiment

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Key Takeaways

Profiled 1 million human B cells in a single experiment
Detected 900,000+ unique paired clonotypes across Type 1 Diabetes, Multiple Sclerosis, Rheumatoid Arthritis, Crohn’s, Celiac, and healthy donors
Achieved sensitive detection of CDR3 regions, clonotypes, and full-length sequences at scale

Evercode BCR uncovered over 900,000 unique paired clonotypes across 24 samples in a single experiment. Negatively selected B cells from 12 healthy donors were purchased, while pan B cells were isolated from 12 autoimmune-diseased human PBMCs and fixed using the Evercode Cell Fixation Kit v3 to preserve cell structure and protect RNA integrity. Fixed samples were stored at -80°C until all were ready for combined processing with the Evercode BCR Mega Kit. Whole transcriptome and BCR-specific libraries were sequenced on the Illumina Novaseq X, and data were analyzed using Parse Biosciences’ Analysis Pipeline v1.3.0. Clustering with Seurat 5.0 showed that the majority of cells corresponded to major B cell subtypes, as illustrated in the UMAP below (Figure 1).

The assay demonstrated high sensitivity, detecting paired heavy and light chains in up to 89% of cells (Figure 2).

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CNV Backbone Spike-in Panels

November 19, 2025/in News, NGS, Partners, Twist/by Harshita Sharma

Stronger CNV Detection — Without Changing Your Exome Workflow.

Large copy number variations can be easy to miss when exome sequencing focuses on exonic regions – leading to blind spots, repeat testing, and extra time interpreting unclear data. By spiking in Twist’s CNV Backbone Panels, you give your exome run the evenly spaced genomic coverage it needs to reliably surface clinically relevant CNVs — especially the ones standard exomes struggle with.

Available in 100 kb, 50 kb, and 25 kb resolutions, the CNV Backbone Panels strengthen your detection sensitivity while keeping your workflow identical. Blend it into your existing exome panel, follow your standard Twist enrichment protocol, and immediately get more confident CNV calls backed by consistent probe tiling across intergenic regions.

But... Why These Panels?

Download Poster on utilization of these panels with exome for cytogenetic research

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Why It Matters to You

Reliable CNV detection isn’t just a technical metric—it directly impacts research outcomes, diagnostic accuracy, and patient care. Standard exome sequencing often misses CNVs due to uneven probe coverage, creating blind spots in your analysis. Twist CNV Backbone Spike-in Panels bridge those gaps, ensuring that even subtle copy number changes are identified, so you can make confident, data-driven decisions.

For labs and clinicians, this means fewer follow-up tests, reduced time spent troubleshooting ambiguous results, and a smoother workflow. You can trust that your exome sequencing captures the variations that truly matter, whether for rare disease research, clinical diagnostics, or high-throughput screening.

Moreover, the ability to choose between 25 kb, 50 kb, or 100 kb resolution gives you control over sensitivity and throughput, aligning with your project goals and patient population needs. Evidence-based validation demonstrates improved detection of CNVs—including those smaller than 50 kb—so your results aren’t just comprehensive, they’re actionable.

By integrating these panels into your existing workflow, you enhance not only the quality of your data but also the efficiency of your lab operations, freeing time and resources for deeper analysis and patient-focused outcomes.

Select the CNV Resolution You Need

Table 1. Example data of Twist CNV Backbone Spike-in Panels. A highly characterized sample set known to contain CNVs (1) and a baseline set of 12 healthy individuals were sequenced with 2×150 reads on an Illumina NovaSeq 6000. The average number of SNVs, INDELs, and CNVs called and sequencing depth at each probe density was determined for each panel when spiked into Twist Exome 2.0 plus Comprehensive Spike-in. CNV calling was performed with a commercially available software solution (2)

(1) Coriell Institute’s CNVPANEL01 – Human CNV Reference Panel.
(2) eVai Platform (secondary workflow), enGenome Software.

A CRE.AI.TIVE application of AI: Engineering a more resilient global food supply

November 17, 2025/in News, Twist/by Harshita Sharma

A CRE.AI.TIVE application of AI: Engineering a more resilient global food supply

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Phytoform Labs harnessed its AI-powered CRE.AI.TIVE platform to develop climate-resilient crops, with a focus on drought-tolerant tomatoes. By rapidly exploring millions of potential sequence edits, the platform identified 2,000 high-potential candidates for wet-lab validation.

To overcome the challenge of synthesizing complex, AT-rich sequences with homopolymers, the team partnered with Twist Bioscience. Twist’s high-fidelity oligos ensured accurate transfer of AI-designed sequences to the lab, enabling efficient MPRA screening in tomato protoplasts.

This AI-guided workflow validated predictions while streamlining experiments—reducing waste, saving resources, and ensuring only the most promising variants progressed to in vivo testing.

Case Study Highlights

AI-driven design of millions of sequence variants
Efficient identification of high-impact edits, conserving time and resources
Ensuring fidelity of AI-generated oligos for complex plant sequences
Insights on impact and future directions

As the official distributor in Australia and New Zealand, Decode Science makes accessing genomics solutions straightforward. Our role is to connect your lab with advanced technologies, ensuring you get the right solution for your sequencing projects—delivered locally with support when you need it.

Modified LongPlex™ Protocol (LongPlex XL)

November 17, 2025/in News, SeqWell/by Harshita Sharma

Modified LongPlex™ Protocol (LongPlex XL)

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This technical note outlines an alternative workflow for generating 10–15 kb HiFi reads from high-quality genomic DNA using the LongPlex Long Fragment Multiplexing Kit. In this approach, LongPlex is used to fragment and barcode samples, and PacBio’s Short Read Eliminator (SRE) is applied to size-select fragments >10 kb before SMRTbell® library preparation.

LongPlex uses plate-based transposase tagmentation for multiplexed fragmentation and barcoding, removing the need for mechanical shearing and allowing barcoded samples to be pooled before SMRTbell prep. This simplifies the workflow, increases throughput, and lowers library prep costs.

The standard LongPlex protocol generates 6–9 kb HiFi reads from high- to medium-quality DNA—ideal for microbial and other small-genome projects. However, users working with higher-quality DNA may want longer HiFi reads to maximize gigabase yield on PacBio systems.

This modified workflow is only suitable for high-quality DNA (Femto Pulse GQN30kb ≥7). Using degraded DNA will result in substantial sample loss during SRE size selection.

As the official distributor in Australia and New Zealand, Decode Science makes accessing genomics solutions straightforward. Our role is to connect your lab with advanced technologies, ensuring you get the right solution for your sequencing projects—delivered locally with support when you need it.

Comparison of Evercode™ WT v3 and Chromium™ GEM-X Single Cell 3’ Kit v4 in Mouse Brain Nuclei

November 13, 2025/in News, Parse/by Harshita Sharma

Comparison of Evercode™ WT v3 and Chromium™ GEM-X Single Cell 3’ Kit v4 in Mouse Brain Nuclei

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Comparison Highlights

Evercode WT v3 delivers superior gene detection in head-to-head sensitivity tests.
Cell type proportions remain consistently represented.
Analysis of differential gene expression shows Evercode WT v3 identifies 2× more genes than competing methods.

Study Overview
Two embryonic mouse brains were sagittally dissected and flash-frozen by a third-party vendor.
One half from each brain was processed by a 10x Genomics certified provider for nuclei isolation and library preparation, while the other halves were processed by Parse Biosciences using their own workflow.
Sequencing was performed by a third-party, and data analysis was completed using each manufacturer’s respective pipeline.

As the official distributor in Australia and New Zealand, Decode Science makes accessing genomics solutions straightforward. Our role is to connect your lab with advanced technologies, ensuring you get the right solution for your sequencing projects—delivered locally with support when you need it.

Optimized CRISPR/Cas9 Gene Knockout PDF

November 13, 2025/in EditCo, News, Partners/by Harshita Sharma

Accelerate knockout experiments with XDel’s next-generation CRISPR design.

EditCo Bio’s XDel technology eliminates the need for guide RNA pre-screening, using a coordinated multi-gRNA design that delivers consistently high on-target editing across immortalized, primary, and iPSC lines. Validated through 768 edited samples and 4,816 NGS libraries, XDel achieves robust knockout efficiency while minimizing off-target effects—saving time and improving reproducibility across diverse cell types.

Validated performance. Proven precision.
With a standardized amplicon sequencing QC workflow and high-throughput automation, XDel enables scalable, high-confidence Cas9-mediated editing for both pooled and single-cell clone analysis. Download the full guide to explore the data, workflows, and results behind EditCo Bio’s high-efficiency gene knockout strategy.

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Excel Sheet: STOmics Validated Tissue List

October 30, 2025/in News, STOmics/by Harshita Sharma

STOmics Validated Tissue List

Our STOmics validated tissue list provides researchers with a comprehensive reference of hundreds of tissue types successfully tested using Stereo-seq, the cutting-edge spatial transcriptomics technology. Each tissue entry includes detailed sample information, experimental parameters, and test results, allowing scientists to make informed decisions before starting their single-cell or spatial transcriptomics experiments. By consulting this list, you can ensure compatibility with your tissue samples and streamline your experimental design.

The list not only highlights tissue types that have been validated but also provides insights into the experimental conditions that yielded the most reliable results. Researchers can leverage this information to optimize sample preparation, sequencing protocols, and data quality control measures. This reduces trial-and-error, saves valuable time and resources, and ensures reproducibility across studies. It is an essential tool for anyone planning to use Stereo-seq for spatial gene expression profiling.

In addition, our STOmics validated tissue list supports better planning for large-scale studies and comparative analyses. By providing a centralized reference for tissue performance, it helps guide tissue selection, anticipate potential challenges, and maximize experimental success. Whether you are exploring new tissue types or scaling up existing workflows, this validated tissue list is your key resource for robust, high-quality spatial transcriptomics research.

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Notices:

The STOmics validated tissue list was generated using standard tissue and sample types, all of which are frozen. Each tissue sample had an area of less than 1 cm² and was sectioned at a thickness of 10 μm. Most experiments were performed using the Stereo-seq Transcriptomics Kit V1.2, with a few using V1.1. Sequencing depth ranged from 1–3 G reads per sample, and data were processed using the Stereo-seq Analysis Workflow (SAW) versions V2.1.0–V5.1.3. Testing was conducted between 2020 and 2022.

Please note that all test parameters and results are highly dependent on the tissue and sample type. This information should be used as a reference guide to help design and optimize your own experiments, rather than as definitive outcomes for all samples.

Key parameters included in the list:

MID (K): Bin200_Median_MID in thousands
GENETYPE (K): Bin200_Median_Genetype in thousands

As the official distributor in Australia and New Zealand, Decode Science makes accessing genomics solutions straightforward. Our role is to connect your lab with advanced technologies, ensuring you get the right solution for your sequencing projects—delivered locally with support when you need it.

Data Set: Parse 10 Million Human PBMCs in a Single Experiment

October 30, 2025/in News, Parse/by Harshita Sharma

Scale Single-Cell Research Like Never Before

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Key Takeaways

Analyze 10 million cells across 1,152 samples in a single experiment
Increase statistical power by profiling more cells per sample
Capture detailed cellular responses to perturbations and drug treatments

Figure 1: Experimental Design Overview
Approximately 10 million PBMCs from 12 healthy donors were treated with 90 different cytokines in a single GigaLab experiment, covering 1,092 experimental conditions.

Cells were thawed, washed, and seeded at 1 million cells per well across 12 plates. After 24-hour cytokine treatment, cells were fixed, barcoded, and processed for whole transcriptome sequencing. Libraries were sequenced on the Ultima Genomics platform, achieving ~31,000 reads per cell, with 62.45% cell retention after barcoding.

Results?

After data processing with the Parse Analysis Pipeline v1.4.0, integration, and classification, 9,697,974 cells across 18 immune cell types were identified—including rare populations that are typically missed in smaller experiments. Each condition yielded a median of 7,400 cells, enabling high-resolution analysis of immune responses.

Differential expression analysis identified how cytokines influenced gene activity across cell types. Many cytokines triggered strong transcriptional responses, with over 50 genes upregulated per treatment.

Figure 2: Single-Cell UMAP Overview
9,697,974 PBMCs from 12 donors were integrated with Harmony, clustered using Scanpy, and manually annotated, revealing 18 immune cell types present across all donors and experimental conditions.

Figure 3: Cytokine-Induced Gene Changes
A heatmap summarizes the averaged number of genes significantly upregulated (log fold change >0.3, p <0.001) for each cell type and cytokine, highlighting which immune cells respond most strongly to specific cytokine treatments.

Example tutorial vignettes from Parse Biosciences and Fabian Theis’ lab at Helmoltz-Munich:

Parse 10M PBMC Cytokines Clustering Tutorial
Joey Pangallo, Efi Papalexi – Parse Biosciences, Seattle, WA
Step-by-step example of analyzing 10 million PBMCs treated with cytokines using the Evercode workflow. Covers data loading, preprocessing, Leiden clustering, and generating UMAP plots with Scanpy.

Parse 10M PBMC Cytokines Clustering Tutorial (Downsampled)
Joey Pangallo, Efi Papalexi – Parse Biosciences, Seattle, WA
Same workflow as above, starting with a downsampled dataset of 1 million cells. Ideal for quicker exploration or limited CPU memory setups.

scCODA Parse 10M PBMC Cytokines
Artur Szałata, Dominik Klein, Soeren Becker, Fabian Theis – Helmholtz-Munich
Demonstrates analysis of cell proportion changes across 10 million PBMCs. Shows how using the full dataset improves statistical significance of perturbation effects. Based on scCODA, a Bayesian model for compositional single-cell data analysis (Nat Commun 12, 6876, 2021).

Parse 10M PBMC Cytokines Dask Workflow
Artur Szałata, Dominik Klein, Soeren Becker, Fabian Theis – Helmholtz-Munich
Walks through preprocessing the 10M cell dataset using Dask. Loads data chunk-wise to reduce memory use and demonstrates highly variable gene selection for downstream analysis.

Dataset License: CC BY-NC 4.0 (non-commercial use). Commercial licensing inquiries: support@parsebiosciences.com

QUICK DOWNLOAD FILES

10M PBMC 12donor 90cytokines h5ad (212 GB)

Parse 10M PBMC cytokines clustering tutorial

Parse 10M PBMC cytokines clustering tutorial on downsampled data

scCODA Parse 10M PBMC cytokines

Parse 10M PBMC cytokines Dask workflow

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Experimental Design:

Results:

Dr. Ebru Boslem

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Dr. Ebru Boslem

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Dr. Ebru Boslem

1. Fine-Tune CNV Resolution

2. Seamless Workflow Integration

3. Evidence-Based Performance

4. Flexible Panel Sizes

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Chris Wicky

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Chris Wicky

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Chris Wicky

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Dr. Ebru Boslem

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Hamza Hassan

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Dr. Ebru Boslem

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Dr. Ebru Boslem

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