_**Impact at a glance:** From a method to image live cells on Xenium slides to the virtual cell itself, these five technology innovations built on 10x Genomics single cell and spatial platforms are redefining what’s possible in science and revealing new depths of cellular and tissue complexity._ 

Innovation is in our DNA at 10x Genomics because innovation is necessary to understand the true complexity of biology. Enabling your most ambitious experiments, creating capabilities that can unlock breakthroughs for human health, and providing access to biological insights we could never access before—these goals motivate everything we do. 

And we love it when our customers share that same vision. In this blog, we highlight customer-developed technology innovations that build on our single cell and spatial platforms. These methods provide creative solutions to access novel analytes or advance multiomic analysis techniques, and they demonstrate critical applications to human health and disease research. 

Keep reading to learn more, or jump ahead to any of our top five innovations from 2025: 

- <a href="#VISTA_FISH">VISTA-FISH</a>: Live-cell imaging on Xenium slides
- <a href="#MALDI_MSI">MALDI-MSI</a>: Same-section Xenium and spatial metabolomics 
- <a href="#REFLEX">REFLEX</a>: TCR-seq enabled with Flex 
- <a href="#CRISPore_seq">CRISPore-seq</a>: Single cell CRISPR screening with isoform detection
- <a href="#The_virtual_cell">The virtual cell</a> 

_\*Note, the following numerical ordering does not ascribe a particular ranking or relative importance to our five single cell and spatial innovations._ 

## <div id="VISTA_FISH">1. Live-cell microscopy on Xenium slides</div>

An imaging-based spatial technique lends itself to integration with other kinds of imaging technology, including video recordings! Video Imaging with Spatial-Temporal Analysis by FISH (VISTA-FISH) is a method that pairs recordings of living, cultured cells with spatial gene expression data from the same cells. The technology, featured in a recent _bioRxiv_ preprint (1), was built on the Xenium Spatial platform and leveraged the Xenium Prime 5K gene panel, providing subcellular spatial resolution of gene measurements in cultured neuron cells placed on Xenium slides. 

The University of Michigan developers built a custom gasket on the Xenium slide to ensure cultured cells grew within the Capture Area. Xenium slides don’t have any cytotoxic materials, allowing the team to culture neurons for 4–6 weeks directly on the slide. Then they performed live-cell imaging using a confocal microscope, spatial transcriptomics using the Xenium workflow following cell fixation, and lastly immunofluorescent imaging. 

The ideal biological applications? The research team behind VISTA-FISH sees its use particularly in linking gene expression to temporal phenotypes, such as rapid increases in calcium concentration, indicative of neuron activity, or morphological phenotypes, such as organelle movement, which are reflective of cell state and function. The readout from VISTA-FISH could reveal molecular pathways regulating these complex cellular functions. With the spatial resolution provided by Xenium, VISTA-FISH could also potentially link subcellular transcript localization with unique differentiation stages and cellular activity.  

The developers added one more analyte to their already impressive spatial multiomics method, enabling even deeper functional genomics characterization through single cell pooled CRISPR screening with VISTA-FISH. They created a strategy to detect single guide RNAs using custom probes designed within a 480-gene custom Xenium panel, then validated the approach with a CRISPR screen exploring the effects of knocking down 24 transcription factors highly expressed in their cultured neuronal cell line. This revealed 4 transcription factors for which knockdown drove a significant reduction in lysosome movement, potentially impairing processes critical for neuronal maintenance. 

Read more about VISTA-FISH **[here](https://www.biorxiv.org/content/10.1101/2025.11.03.686351v1.full)**. 

## <div id="MALDI_MSI">2. Spatial metabolomics meets Xenium RNA profiling</div>

This next innovation is cool because it uses lasers and Xenium. Enough said?  

To get a little more specific, MALDI-MSI is a technique that extends spatial analysis into the world of metabolomics, the study of the biomolecules and proteins involved in keeping cells alive, from energy carriers like ATP, to signaling molecules and waste products. A team of researchers from Maastricht MultiModal Molecular Imaging (M4I) Institute at Maastricht University combined MALDI-MSI with Xenium spatial transcriptomics, building out same-section, same-cell multiomic capabilities that provide a more comprehensive profile of the biology of the tissue microenvironment.

What is MALDI-MSI? _Matrix-assisted laser desorption ionization mass spectrometry_ _imaging_ is a technique that allows researchers to resolve the location and identity of lipids, metabolites, and proteins within tissue sections at subcellular resolution. Essentially, the method uses lasers to charge those molecules, which are then accelerated through a mass spectrometer to measure their mass-to-charge ratio. Combined with imaging, this technique allows researchers to map the spatial distribution of any specific mass-to-charge value, then work backwards to identify what that molecule corresponds to. 

Importantly, the Maastricht University team performed this technique on tissue sections directly on _Xenium slides_, then followed up the MALDI-MSI run with single cell spatial gene expression analysis. This approach reduced the variability that occurs from splitting the methods between serial sections. 

MALDI-MSI also retained RNA content, making post-run Xenium analysis biologically meaningful. The team simply performed a washing step on Xenium tissue slides before they were fixed and permeabilized. While they observed a 30% reduction in transcripts per cell in Xenium data, cell segmentation and cell-type identification remained the same between samples that did and didn’t undergo MALDI-MSI, confirming the approach was non-destructive and retained important biological information.

Validating this approach in fresh frozen mouse brain and human glioblastoma samples, the research team demonstrated its ability to link specific cell types with distinct combinations of transcriptomic profiles and molecular ions. This provides a means to characterize cell types beyond RNA alone. MALDI-MSI also highlighted regional metabolomic states within distinct tissue areas, providing another readout, in addition to RNA and cell clustering, to explore tissue-wide changes resulting from pathology like glioblastoma (2). 

Learn more about this method **[here](https://www.nature.com/articles/s41598-025-26735-1)**. 

## <div id="REFLEX">3. REFLEX enables single cell TCR sequencing with Flex</div>

Our next innovation, REFLEX, or REpertoire-FLEX, provides access to a valuable transcript class that the probe-based Flex chemistry doesn’t typically capture: T-cell receptor (TCR) sequences. 

The method, developed by researchers from the Allen Institute for Immunology, builds on the best of the Flex platform, which supports sample fixation for batching and large-scale workflows, and sensitive transcript detection in FFPE samples through a probe-based chemistry. But it also brings in the best of a 5' reverse transcription (RT)-based system, enabling de novo V(D)J discovery and broad exploration of T-cell immunity. 

Some quick background on V(D)J discovery: V(D)J recombination creates such vast sequence diversity that it’s essentially impossible to design specific oligonucleotide probes that hybridize to these targets. You can’t design probes for sequences that you don’t know will exist! You need a reverse transcription-based system to capture whatever sequence is actually there, including the variable CDR3 sequences in TCRs that define clonal identity.  

Allen Institute researchers figured out how to merge the Flex probe-based chemistry and the Universal 5’ RT-based chemistry together in the same workflow by introducing primers targeting the 3’ constant regions of the TCR, _TRAC_ (T-cell receptor alpha constant) and _TRBC_ (T-cell receptor beta constant). They also designed overhanging 5’ primers encoding 10x sample-specific barcodes (allowing sample multiplexing for scaling) and a constant sequence to mediate binding to pre-annealed DNA splinting oligos that served, downstream in the workflow, as a bridge between TCR sequences and Gel-Bead oligos during GEM incubation. With these primers in place, they performed probe hybridization then reverse transcription to generate cDNA libraries that also included TCR sequences with variable regions. 

This creative technical solution allowed the team to profile over 2 million human PBMCs in one run (pooling cells from 16 samples with unique sample barcodes and loading the maximum recommended input of 256,000 cells per GEM well). They observed a 15-fold proportional increase in cells captured per well compared to an equivalent Universal 5’ experiment, and better performance for TCRα/β chain detection, with 105 more clones detected by REFLEX (_most_ representing novel TCR sequences). 

These staggering results point to the huge potential of REFLEX to unlock new levels of performance, scale, and cost-efficiency for comprehensive, single cell T-cell profiling in academic and translational research contexts. 

 Explore the REFLEX _bioRxiv_ preprint **[here](https://www.biorxiv.org/content/10.1101/2025.10.24.684243v1?ct=)**. 

## <div id="CRISPore_seq">4. Single cell CRISPR screen combines short-read and long-read sequencing to detect isoforms</div>

Our next innovation, CRISPore-seq, expands on already powerful multiomic single cell CRISPR screening capabilities, which provide a readout of perturbations, whole transcriptome gene expression, and cell surface protein markers in the same single cells. But what short-read sequencing can’t do alone in methods like ECCITE-seq, this new CRISPR innovation overcomes by **combining short-read and long-read sequencing in the same workflow** to obtain transcript isoforms along with multiomic single cell perturbation readouts. 

A research team from the New York Genome Center and NYU performed a CRISPR perturbation screen in a human breast cancer cell line on RNA-binding proteins that are known to regulate alternative splicing, a core process that generates transcript isoforms. They designed a single guide RNA library to perturb a diverse set of these proteins, then performed a single cell CRISPR screen built on the 10x Genomics Universal 5’ workflow that, following GEM-RT cleanup and cDNA amplification, split cDNA sequencing between both short-read and long-read platforms. This allowed them to achieve broad coverage of multiomic readouts across individual cells, including whole transcriptome gene expression, cell surface markers through oligo-conjugated antibodies, and sgRNA that defined the perturbation, while also capturing full-length transcript isoforms. 

Their method confirmed the important gap that long-read sequencing fills: long-read sequencing, for example, revealed two major isoforms for the gene _NPM1_, which diverged at the 3’ end. 5’-based short-read sequencing alone could not resolve this transcript diversity. Long-read sequencing also revealed functional consequences of some isoform variants: for example, a perturbation of the gene _SF3B4_ led to an isoform of _CCND1_ with a skipped exon, which may drive cell cycle arrest and an accumulation of cells at the G1 checkpoint (4). This confirmed the value of CRISPore-seq to uncover another layer of perturbation effects by providing access to transcripts that may play important roles in biology, but would go undetected with short-read sequencing alone.  

Keep reading about this new method [here](https://www.biorxiv.org/content/10.1101/2025.11.25.690515v1). 

## <div id="The_virtual_cell">5. Building biology’s digital twin: The virtual cell</div>

The [_Nature_ Methods to Watch](https://www.nature.com/nmeth/articles?type=method-to-watch&year=2025) list from 2025 included an innovation (in development) that represents the next moonshot goal of biological science: a **virtual cell model** that can accurately reflect comprehensive cellular and molecular phenotypes, and respond like a true, living cell to internal perturbation, drug exposure, and more (5).  

This goal is within reach because of the historic intersection of innovations in high-throughput cellular profiling technology and computational tools like machine learning and artificial intelligence. Because the key to building a good virtual cell is high-quantity, high-quality cellular data. 

This core need has led many research groups seeking to develop biological models, from virtual cells to predictive models of the tumor microenvironment, to use 10x Genomics single cell and spatial technology. 

Commenting on the choice to use Visium spatial transcriptomics as part of a large-scale cancer profiling study, MOSAIC, Dr. Raphael Gottardo, Professor of Biomedical Data Science at the University of Lausanne, said, “At the end of the day, it's all about the data… you need to have high-quality data. And I think having the right instruments, the right technologies, will get you there.” 

Data quality and an ability to achieve greater scale are drivers of continuous improvement and innovation at 10x Genomics, leading to choices in foundational chemistry underlying some of our technologies that ensure highly sensitive gene detection (such as probe-based chemistries), or additional product configurations that enable massive scale, such as the new [plate-based multiplexing capabilities with Flex](https://www.10xgenomics.com/products/flex).  

There’s so much going on in this space, but a quick hit-list of the AI initiatives that build on the capabilities provided by 10x Genomics technology include: 

- The Arc Institute’s Virtual Cell Atlas 
- The Billion Cells Project from the Chan Zuckerberg Initiative
- MOSAIC (Multi-Omics Spatial Atlas In Cancer), an Owkin initiative
- The TISHUMAP initiative from A*STAR, part of the Genome Institute of Singapore 
- Xaira Therapeutics’ X-Atlas/Orion Perturb-seq atlas 

Learn more about these initiatives and how 10x technology is fueling a new frontier of biological modeling and drug discovery at **[10xgenomics.com/ai](http://10xgenomics.com/ai).**

## Looking ahead to innovations in 2026

While we’re beyond impressed and inspired by the innovations our customers and scientific partners have developed in 2025, we know it’s just the beginning. New technology and new ideas come together to do what could never be done before. 

That’s the pattern of innovation that we have seen at 10x Genomics, and in our incredible customers, time and time again. So here’s to a bright future where we continue to resolve biological complexity in unprecedented ways, all towards the goals of deeper insights into life on earth and transformational change in medicine and human health. 

**References:** 

1. Lee K, et al. VISTA-FISH: Video Imaging with Spatial-Temporal Analysis by Fluorescent In Situ Hybridization. _bioRxiv_ (2025). doi: 10.1101/2025.11.03.686351
2. Hendriks T, et al. One section, two worlds: single-cell integration of MALDI-MSI and spatial transcriptomics on the same single tissue section. _Sci Rep_ 15: 42660 (2025). doi: 10.1038/s41598-025-26735-1
3. Hart M, et al. REFLEX, a novel immune profiling assay, combining TCR repertoire and multiome at massively scalable single-cell resolution to catapult exploration of T-cell derived immunity. _bioRxiv_ (2025). doi: 10.1101/2025.10.24.684243
4. Müller S, et al. Transcriptome-wide profiling of alternative splicing regulators with CRISPore-seq. _bioRxiv_ (2025). doi: 10.1101/2025.11.25.690515
5. Bunne C, et al. How to build the virtual cell with artificial intelligence: Priorities and opportunities. _Cell_ 187: 7045–7063 (2024). doi: 10.1016/j.cell.2024.11.015
6. Tang L. The virtual cell. _Nat Methods_ 22: 2493 (2025). doi: 10.1038/s41592-025-02951-5

**About the author:**

![Olivia received a BA in Molecular & Cell Biology with an emphasis in Immunology and an English minor from UC Berkeley. She began writing as an undergraduate for the High Performance Computing program, interviewing Berkeley professors, postdocs, and graduate researchers about how they were using computing to analyze their data and model experimental systems, from weather forecasting to nuclear reactions. This inspired her interest to tell stories about innovative science and the technology that makes it possible, which led her to 10x Genomics where she has been writing for the last 6 years.](https://cdn.10xgenomics.com/image/upload/c_thumb,w_200,g_face/v1624412499/people/olivia-habern.jpg)


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