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Research Article
Anna K. Casasent*1, Danielle L. Stolley*1, Basant T. Gamal*2, Arnold Dahay2, Samuel Mok2, Lidia Rocha3, Vincent Li3, Arizona T. Nguyen3, Boyu Zhang4, Clay T. Brasuell4, Erika J. Thompson4, Thomas Huynh3, Jared K. Burks1, Sammy Ferri-Borgogno2
1Department of Hematopoietic Biology and Malignancies,The University of Texas MD Anderson Cancer Center, 2Department of Gynecologic Oncology and Reproductive Medicine,The University of Texas MD Anderson Cancer Center, 3Department of Veterinary Medicine & Surgery,University of Texas MD Anderson Cancer Center, 4Department of Genetics,The University of Texas MD Anderson Cancer Center
Erratum Notice
Important: There has been an erratum issued for this article. View Erratum Notice
Retraction Notice
The article Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size (LEfSe) in Microbiome Data (10.3791/61715) has been retracted by the journal upon the authors' request due to a conflict regarding the data and methodology. View Retraction Notice
This article presents a protocol to perform comprehensive spatial transcriptomic profiling of host, viral, fungal, and microbiome RNA from formalin-fixed paraffin-embedded tissue sections using Stereo-seq, which enables high-resolution mapping of diverse transcriptomes while preserving tissue architecture.
The spatial composition of cells within the host, as well as the bacteria or viral loads within the tissue, can impact the interaction of cell types and analytes that drive the cell through cell-type-specific processes. Stereo-seq for FFPE tissues uses random priming to spatial barcodes, which is different from standard spatial transcriptomics methods, which use A'tailing to capture messenger RNA (mRNA) or probe-based to capture species-specific transcripts. These methods do not embrace the more current knowledge about the impact and importance of other types of RNA from long non-coding RNA, mitochondrial RNA, microRNA, or other species, RNA, such as microbial, viral, and fungal. Outlined here is a step-by-step procedure from tissue sectioning through library preparation for spatial species-agnostic stereo-seq application with RNA detection using a randomer probe methodology, which is compatible with formalin fixed paraffin embedded (FFPE) tissues. This Stereo-seq method has a random capture bead of 0.22 µm in size that reoccurs in an array every 0.5 µm, allowing for subcellular resolution from a sequencing-based technology. As this method detects both host and non-host RNA, the protocol requires specific considerations to allow for the determination of what is inside the tissue versus what was deposited on the tissue during the collection, preservation, cutting, and detection process (i.e., environmental and handling contaminants). Lastly, this protocol allows one to have high (single-cell) level resolution of multi-RNA species within a spatial context, providing insight into the intra- and inter-actome of cell types and pathological species.
Spatial-omics technologies have revolutionized the study of tissue cellular heterogeneity by enabling the simultaneous quantification of multiple targets, DNA (genomics), RNA (transcriptomics), metabolites (metabolomics), or protein (proteomics)1. The resolution of these different assays often varies, providing different limitations when measuring analytes, either with increased sparsity when measuring single cell2,3 or native spatial context4. In contrast, bulk methods such as RNA-sequencing only provide average gene expression across entire samples and obscure cellular heterogeneity2, or single-cell RNA-sequencing (scRNA-seq)2,3, which requires tissue dissociation for single-cell sequencing, often requires filtering. Therefore, these methods exclude large cells and aggregates, while bioinformatic methods try to remove fragments and doublets, all of which induce biases5,6,7. Spatial transcriptomics retains tissue architecture providing critical insights into cellular organization and interactions. Most of the non-targeted spatial sequencing methods do not have single-cell resolution, thus requiring deconvolution of spatial capture areas that containing 10 to 100 cells8,9,10. However, in the last decade, spatial transcriptomics has outpaced the needs for these methods, as there have been advancements in resolution, larger areas of capture, and the integration of multi-omics approaches, either using experimental strategy or novel techniques1,11,12.
Spatial Enhanced Resolution Omics-sequencing (Stereo-seq) provides nanoscale resolution (500 nm) and a centimeter-scale capture area, with species-agnostic whole transcriptomic capture allowing for the capture of coding, non-coding, and non-host RNA13. This method is currently the highest resolution for spatial transcriptomics and has options to provide the largest fields of view. Yet, this method has some notable limitations, including time and labor requirements of around 15 h hands-on time, spanning 4 days to complete the sequencing-ready libraries. In comparison, other sequencing-based spatial transcriptomics technologies, such as probe-based spatial transcriptomics, polyA-capture array-based spatial transcriptomics, Slide-seq, and deterministic barcoding in tissue for spatial omics sequencing (DBiT-seq) typically require 5 to 8 h of hands-on time over 2 to 3 days (Table 1). For many researchers, these advances outweigh the technical challenges and are ideal for constructing full transcriptomics spatial atlases to study disease progression at cellular and subcellular levels, particularly if layered with other technologies1.
The Stereo-seq protocol has possibilities beyond traditional mRNA transcriptomics, which relies on poly-T probes for capturing polyadenylated mRNA transcripts. These methods can be modified by adding polyadenylation to fresh tissues14 or species-specific probes for target predefined gene sets15 for spatial expression analysis. Instead, Stereo-seq uses random nucleotide probes to select diverse RNAs regardless of species, including microbial RNAs, fungal RNAs, viral RNAs, and non-polyadenylated RNAs such as enhancer RNAs, circular RNAs, and long non-coding RNAs (lncRNAs)16. Theoretically, it is possible to capture smaller RNAs or reparative elements such as small interfering RNAs (siRNAs), microRNAs (miRNAs), and transposons17. However, currently the bioinformatics and mapping of these RNA remain challenging and are often filtered out during the initial alignment , which typically uses 30 bp cutoff.
Outlined here is a detailed protocol, with our modifications for a manufacturer's random nucleotide capture on DNA nanoballs (DNB) chips to ensure better understanding and handling of a Stereo-seq protocol illustrated in Figure 1. This protocol includes strategies for molecular biologists for testing new FFPE tissues on Stereo-seq with random nucleotide capture to produce more consistent, higher yields from lower quality archived samples and fatty tissues. The outlined protocol is optimized for FFPE samples from most organs, regardless of species of origin. However, this protocol does not include current modifications needed to handle difficult tissues such as bone marrow, which require decalcification.
No clinical data are provided in this methods paper for the treatment-naive high-grade serous ovarian carcinoma patient specimen. However, associated clinical data were collected following protocols approved by the Institutional Review Board (IRB) at The University of Texas MD Anderson Cancer Center, Department of Gynecologic Oncology and Reproductive Medicine, from participants who provided written informed consent.
NOTE: It is recommended to wear a surgical mask and gloves during the entirety of the protocol to prevent RNase contamination. Researchers with long hair are recommended to pull it back or even wear a hairnet. Always use sterile nuclease free water (NFW) and nuclease free, low-binding DNA/RNA tubes to maximize yield. Non-mammalian tissues, such as plant tissues, may require adherence optimization, involving poly-L-lysine (PLL) and extended permeabilization times due to cell walls. While molecular capture remains theoretically feasible, experimental validation is needed as plant tissues remain untested in our current workflow.
1. Assess RNA quality and RNA fragment distribution value 200 scores
2. Workspace cleaning
3. Stereo-seq random oligonucleotide capture chip (N-chip) preparation
4.Tissue sectioning and mounting
5. Deparaffinize slides
6. (Optional) Nuclear imaging and ssDNA staining
NOTE: Allow the FFPE decrosslinking reagent to reach room temperature ( ~25 °C) before use in 6.1. Other staining solutions are possible, but fluorescence nuclear staining is currently required for single-cell segmentation and cell-binning of transcriptomic data.
7. Decrosslinking process
NOTE: Avoid touching or tilting the chip during this step, and pipette gently.
8. Permeabilization and reverse transcription
NOTE: For steps 8.1-8.11, pipette slowly and do not touch the chip. Excess pressure will cause diffusion or marks in the DNBs.
9. cDNA release and purification
NOTE: For steps 9.6-9.7, pipette slowly and do not touch the chip. Excess pressure will cause diffusion or marks in the DNBs.
10. Collect the cDNA release mix
11. Collect the cDNA bead clean up
12. cDNA amplification
13. Library preparation (Day 4)
14. Library amplification bead clean up
15. DNBSEQ-T7RS system for sequencing Stereo-Seq Random Oligonucleotide Primed DNBs
The data below demonstrates the ability to perform single-cell segmentation using the SAW pipeline and mapping to non-poly adenylated RNAs such as tRNA (TRDMT1) from an FFPE section. The unbiased data from Stereo-seq allow one to examine the importance of not just mRNAs but also tRNAs, rRNAs, and non-host RNAs (if pertinent). Using the direct output from the SAW pipeline16 and the stereopy for quality assessment of the Stereo-seq output in Figure 3A-G, we generated standard spatial overviews, gene expression, as well as clustering and QC from the cell bins (single nucleus overlays created via the saw nuclei image). The number of transcripts detected varies depending on tissue type: immune-dense tissues typically exhibit lower transcript counts (approximately 100 genes per cellbin), whereas aneuploid tumor cells can have as many as 1,000 transcripts per cellbin. Please note that the clustering of this sample would still require bioinformatics processing to provide cluster identities, as the SAW default resolution is very high (0.8 to 1), and filtering the data based on read counts, poorly defined cells/nuclei are recommended using Python packages such as stereopy4. Cellbins containing greater than 100 genes are considered acceptable for most analysis, as this data is similar to single-cell nuclei data. During filtering, classical single cell metrics can be used, including gene per cellbin of < 100 (removing low cellbins), cellbin area to remove undefined cellbin, and duplicates or hot spots cellbin. Other spatial analysis or filtering can include tissue edge effect, space to nearest cells. Other cell analysis, such as a high percentage of mitochondria per cellbin is not suggested (Figure 3H).
Next, the cellbin using the proprietary software (StereoMap4) and stereopy (coding-based) provides examples of spatial visualization. Figure 4A demonstrates the red box using nuclei ssDNA image. The interactive software shows the spatial distribution of putative cell types, such as Figure 4B and the single gene interactively Figure 4C, such as a non-poly adenylated RNA tRNA aspartic acid methyltransferase 1 (TRDMT1). This transcript is also visualized across the whole tissue using stereopy Figure 4D. However, a more detailed analysis of the spatial relationships of the single cell and transcriptomics is required to provide insight into the importance of any transcripts observed.

Figure 1: Workflow diagram for the protocol. (A) Sample selection and capture chip setting. (B) Day 1: capture chip preparation, sectioning, and overnight baking (during this time, the chip should not be touched). (C) Day 2: Short bake, deparaffinization, staining, and imaging (during this time, touching of the chip should be limited to prevent scratch artifacts). (D) Day 2: decrosslinking, methanol fixation, permeabilization, FFPE reverse transcription should be done with slow even pipetting to prevent diffusion artifacts, (E) Day 3: cDNA release prerelease slow pipetting, post release vigorous pipetting to collect all cDNA, cDNA cleaned up, cDNA amplification and a second clean up, (F) Day 4 is the QC for the cDNA, library barcode selection, NGS library preparation and submission. During Day 1-2, it is very necessary to be very careful with the slide and chip and not scratch the surface. However, in the cDNA release step (when removing the cDNA), it is necessary to be very vigorous when pipetting to get decent yields. Please click here to view a larger version of this figure.

Figure 2: User innovations. First, the application of resin to be used when clearing with xylenes (A) adding resin, (B) curing resin, (C) cleaning resin after curing. Second, the application of PLL (D) adds PLL to the coat slide. The third is the slide drying technique (E) angle and closeness of compressed air during drying. The last adjustment of a silicone chamber for the rehydration process, (F) placing a cut silicone chamber round chip area, (G) lift test to check silicone chamber adherence, (H) adding or removing liquid from the silicone chamber without touching the chip. Please click here to view a larger version of this figure.

Figure 3: Quality control assessment. This figure covers basic outputs from stereopy and saw to assess the quality of the sample. First (A) ssDNA nuclear staining image required to create single-cell level bins (cellbins) across the tissue. It is a confocal microscope stitched single-channel ssDNA staining image for the nuclear location and track lines. The stereopy (B) spatial gene expression capture map with the number of genes captured per cellbin. SAW output showing pre filtered pipeline for cellbin output including (C) single cell level spatial lieden clustering, (D) lieden cluster UMAP, and (E-G) violin plots of (E) molecular identifier count, (F) gene type count and (G) cell area distribution per cellbin. Lastly, a stereopy quality control metrics standard for single cell sequencing (H) percentage mitochondrial by total count by cellbin. Please click here to view a larger version of this figure.

Figure 4: Simple spatial visualizations. This figure provides a (A) ssDNA nuclear staining image for orientation, with the red box highlighting the regions zoomed in regions in B and C. Images (B-C) provide a StereoMap 4 software, which provides interactive user visualization, (B) the cellbin with liaden cluster labels, (C) with expression levels of tRNA aspartic acid methyltransferase 1 (TRDMT1) with cellbins. Lastly, (D) shows a stereopy spatial expression of TRDMT1 across the whole tissue. Please click here to view a larger version of this figure.
| Method Name | Tissue Fixation | FOV (mm) | Resolution (µ m) | Captures | Hands on (hrs) | Protocol days | Citation |
| Stereo-seq A’Tailing Capture | Fresh-Frozen Fresh-Frozen PFA fixed | 0.5 x 0.5 to 13.2 x 13.2 | 0.5 (often binned to 25 or single cell) | Poly adenylated RNAs (e.g. mRNA) | ~10 | 2-3 | A. Chen et al. |
| Cell(2022) | |||||||
| Stereo-seq Random N Priming (Stereo-seq V2) | FFPE (DV200 >= 30) | 0.5 x 0.5 to 13.2 x 13.2 | 0.5 (often binned to 25 or single cell) | Total RNA | ~16 | ~4 | S. Ferri-Borgogno et al.19 |
| Cancers(2024) | |||||||
| Curio Bio Trekker | FFPE | 10 x 10 | single droplet / nuclei level | Whole Transcriptome (probe based) | ~9 (~2 hour add on to single cell library prep workflows) | ~2 | A. J. C. Russell et al. |
| (slide-Tag) | Nature(2024) | ||||||
| Curio Bio SEEKER | Fresh-Frozen | 6 x 6 | Whole Transcriptome (probe based) | ~8 | ~2 | Curio Bioscience (2023) | |
| Visium HD | FFPE/Fresh-Frozen | 6.5 x 6.5 | 2 | Whole Transcriptome (probe based) | ~10 | 2 | 10x Genomics (2022) |
| (usually binned to 64) | |||||||
| Visium Standard A’tailing | Fresh-Frozen | 6.5 x 6.5 | 100 center to center | Poly adenylated RNAs (e.g. mRNA) | ~10 | 2 | L. Stenbeck, F. Taborsak-Lines, S. Giacomello Heliyon(2022) |
| Visium Cytassist | FFPE/Fresh-Frozen | 6.5 x 6.5 | 100 center to center | Whole Transcriptome (probe based) | ~10 | 2 | 10x Genomics (2022) |
| DBiT-seq | Fixed frozen | varies | 10 | Transcriptome & Proteome | ~8 | 1-2 | G. Su et al. STAR Protoc. (2021) |
| Slide-seq V2 | Fresh frozen | ~6 | 10 | Whole Transcriptome | ~8 | ~2 days | R. R. Stickels et al. |
| Nature Biotechnology(2021) |
Table 1: Method comparisons of Sequencing-based Spatial Transcriptomics methods. This table summarizes key features of major sequencing-based spatial transcriptomics platforms currently available. Methods are compared by tissue fixation compatibility, field of view (FOV, in mm), spatial resolution (in µm), RNA species captured, estimated protocol hands-on time (in h), total protocol duration (in days), and relevant citations or commercial protocol1,13,20,21,22,23,24.
The Stereo-seq protocol is highly detailed and involves several critical steps that require precision, timing, and a clean environment to ensure success especially with microbiome or non-host profiling that may require specific precautions.
The use of nuclease free water during all steps is required, and the capture chip must not be touched with anything other than the sample and reagents. Exercise extreme caution when using the cassette assembly, taking time to utilize practice chips. A scratch on the tissue area will ruin tissue spatial resolution, and a scratch on the track lines can likely prevent the spatial resolution or the sample from being automatically aligned. Further, for high RNase/DNase tissues such as pancreatic or liver, changes of water during sectioning are recommended. Additionally, to maintain RNA quality it is suggested to store FFPE tissue blocks with a small amount of wax-seal on the face, at 4 °C, and in a desiccator to prevent degradation. If stored this way, the samples will still need to be rehydrated on the surface and equilibrated to cutting room temperature before using25,26,27. The cutting room should be at 22 to 25 °C. Furthermore, the samples best suited for Stereo-seq are ones with a DV200 score of higher than 50, but scores with DV200 over 30 may also be sequenced with limited results. If microbiome is of interest, it is best practice to not reuse any materials and only use fresh reagents, wear masks, and keep areas clean if not sterile for the whole process.
For single-cell resolution and cellular binning (cellbin), imaging of the cell nuclei with ssDNA dye is required. However, the concentration of this dye is camera-specific, and dependent on the dynamic range that can be imaged by the system. This requires titration and testing of dye concentrations for each new device, typically a 1:2 to 1:10 dilution of the ssDNA dye from manufacturer recommendations. During imaging, it is recommended to manually focus on 3-5 areas without tissue and 4-6 areas with tissue; however, most consistent results occurred using a 13-point setup to ensure quality control with clear cellular focus, as both the track lines and tissue must be found in the focal depth.
Certain reagents are more sensitive to timing than others. Specifically, the permeabilization reagent must be resuspended in a 0.01 N HCl solution (pH 2.0 +/- 0.1). Store the aliquoted 10x PR solution to avoid degradation from freeze-thaw cycles. Timing varies by tissue type, with thicker sections requiring between 15 and 30 min when embedded in soft paraffin. Handle chips carefully post-permeabilization to avoid damage, diffusion, and artifacts from disturbing the chip/tissue surface. The sequencing strategy for the library barcode mix is a very important pooling strategy for sequencing and multiplexing. If sequencing a single sample on a single lane, it is easiest to just use a barcode set (BC1-4, BC5-8, BC9-12, or BC13-16). Other strategies are detailed in the manufacturer's handbook.
The addition of a ceramic resin sealant around the chip can help prevent xylene's penetration of the adhesive that seals the chip to the slide (Figure 2A-C). In addition, the use of poly-L-Lysine is recommended for high fatty tissues and fragile tissues, but may lead to issues with capture due to residues from the PLL drying processes (Figure 2D-E). As such, it is not recommended for all tissues. To reduce reagents, utilize a silicone chamber (Figure 2F-H), which decreases the volume from 30 mL to 500 µL of reagent needed, specifically for microbiome work, which requires fresh reagents for each slide. It is recommended to do additional filtration of reagents when trying to capture microbiome data. Further, utilizing sealed ethanol that is only accessed via a needle/syringe will provide cleaner reactions than ethanol that is open to the air. SPRI beads concentration can be modified by decreasing the concentration to select longer fragments, but this will reduce the overall yield. This protocol was modified on the SPRI bead steps to have a more consistent yield by warming the beads to (~30 °C) and using double elution to increase yield consistency.
Stereo-seq is currently one of the highest resolutions with the largest capture areas for sequencing-based spatial transcriptomics (SST) methods. It offers nanoscale resolution (200 nm nanoball spot size with center-to-center distance of 500 nm), species-agnostic capture, and a large field of view (up to 13 cm x 13 cm). By comparison, Slide-seq V2 achieves ~10 µm resolution with high sensitivity (~50% RNA capture efficiency) but is limited by smaller capture areas with bead-based barcoding24. Similarly, probe based whole transcriptome capture methods and spatial transcriptomics A'tailling capture method provided robust whole mRNA transcriptome profiling using A-tailing capture, but at a much lower resolution (55 µm spot size with center-to-center distance of 100 µm), requiring computational methods to deconvolute the cell mixtures per spot. The newer whole genome probe-based capture methods have improved resolution of 2 µm tiles (recommending 8 µm x 8 µm) and rely on species-specific probe panels mostly focused on protein coding genes; however, the method tends to be more robust and less finicky. Another SST method, DBiT-seq, uses microfluidics-based spatial barcoding to achieve ~10 µm near single-cell resolution with flexibility in experimental design, but lacks the nanoscale precision and scalability of Stereo-seq28. Other cellular-level resolution SST platforms with cellular dissociation are the easiest to combine with single-cell sequencing data, but this method has many of the disadvantages of single-cell sequencing due to its methodology of disaggregation, filtering, and profiling using single-cell RNA methods. Lastly, Slide-tag (10 µm resolution) is more cost-effective but currently does not match Stereo-seq's nanoscale capabilities28.
Aside from SST methods, probe-based methods excel in targeted transcript detection with high sensitivity but are limited to predefined probes, although some have expanded to up to 5,000 probes panels29,30,31. Still, Stereo-seq provides unbiased whole-transcriptome analysis limited to SST methods28,32. Imaging-based methods33,34 achieve single-molecule or single-cell resolution but are constrained by multiplexing limits and imaging throughput. Overall, Stereo-seq is a labor-intensive process, including multiple time-sensitive steps that are extremely influenced by the hands of the operator for the duration of the multiday protocol. The protocol capture rate for each individual space will not be as sensitive as target probe-based methods or imaging methods, but offers unparalleled resolution and unbiased capture in untargeted spatial transcriptomics, as highlighted by its species-agnostic capture ability. The protocol involves a multi-day workflow with numerous steps critical steps, including tissue preparation for sectioning and adhesion, deparaffination, rehydration and staining of the tissue before time-sensitive fluorescent imaging, followed time and pH sensitive by tissue permeabilization, RNA capture, in situ RNA amplification, cDNA release, clean up and amplification, and lastly by library preparation. All of which require meticulous attention, and unique technical skills often best performed by different people, each with detail and precise timing to ensure high-quality results. Many of these steps are highly time-sensitive, particularly tissue permeabilization, RNA capture and enzymatic reactions, where delays or deviations can lead to RNA degradation or loss of spatial resolution. In addition, after permeabilization, there is an increased chance of diffusion the more the slide is moved. Lastly, while library preparation itself does not demand extensive or specialized equipment, relying primarily on standard laboratory tools such as thermocyclers, pipettes, and magnetic racks for bead purification, it still requires an experienced operator to prevent RNA loss, PCR bubbles, or drying during amplification, and understanding of the issues such as uneven amplification, tissue sectioning artifacts, or contamination. This reliance on skilled personnel adds to the complexity of the protocol.
The authors declare that this work was not funded by Complete Genomics. However, Complete Genomics is supporting the costs of publication of this article. They were not given an advanced copy nor input on the content.
This research was funded in part by the Ovarian Cancer Research Alliance (OCRA 811621 and 891490), the Sie Foundation, and the Stephanie C. Stelter Endowment Fund. This research was performed in collaboration with the Flow Cytometry and Cellular Imaging Core Facility, Department of Veterinary Medicine & Surgery and Advanced Genomics Technology Core, which is supported in part by the National Institutes of Health through M. D. Anderson's Cancer Center Support Grant P30 CA016672 and Jared Burks' NCI's Research Specialist 1 R50 CA243707-01A1.
We also would like to thank Compete Genomics, specifically Brandon Vanderbush and Tanzeen Yusuff for technical training and troubleshooting, as well as Jia "Jackie" Zhao, Yongfu Wong and Erin Petrilli. The author(s) received a set of FFPE OMNI and sequencing reagents, as well as access to the T7 Early Access Program, from Complete Genomics at a discounted price. In addition, some reagents were provided free of charge for use in this study.
| 1.5 mL centrifuge tubes regular DNAse/RNAse | Thermo Fisher Scientific / Invitrogen | 3456 | To mix reagents in (other bands are fine) |
| 100% Ethanol | Sigma-Aldrich | E7023 | Molecular grade |
| 15mL Conical Sterile Polypropylene Centrifuge Tubes | Thermo Fisher Scientific / Invitrogen | 339650 | Used for aliquots |
| 20x Concentrate Saline Sodium Citrate (SSC) Buffer | Sigma-Aldrich | S6639-1L | Used to make 5X SSC for staining and 1X SSC for wash steps and coverslip removal. |
| 2100 Bioanalyzer instrument | Agilent | Discountinued | Instument: Bioanalyzer |
| 3 Well Chamber, removable | Ibidi | 80381 | Silicone chamber to reduce volumes during rehydration and methanol fixation. |
| 405nm UV Light Source | Various | N/A | For curing UV resin |
| 50mL Conical Sterile Polypropylene Centrifuge Tubes | Thermo Fisher Scientific / Invitrogen | 339652 | Used for aliquots and for deparaffinization |
| 70% Sterile Isopropanol Alcohol | Texwipe | TX3270 | For surface decontamination |
| Agilent High Sensitivity DNA Kit | Agilent | 5067-4626 | For Bioanalyzer characterization of cDNA and libraries |
| Agilent RNA 6000 Pico Kit | Agilent | 5067-1513 | For Bioanalyzer characterization of RNA |
| Aluminum Foil | Various | N/A | To cover slide during ssDNA staining. |
| Beckman Coulter SPRIselect | Beckman Coulter Life Sciences | B23317/B23318/B23319 | SPRI Beads cDNA and Library Clean up |
| Bleach | Various | N/A | Cleaning workstation removing microbiome contamination |
| CONSTIX Sealed Foam Swabs | Contec | 19161023 | For precise resin cleaning |
| Coverslip | Sigma-Aldrich | BR470045-2000EA | |
| Desiccant Packs | Various | N/A | RNase free, dust free desiccant packs. |
| Disposable Face Mask | Thermo Fisher Scientific / Invitrogen | 12-888-001 | Protection, RNAse contamination and & sterility |
| DNA Erase Wipes | Sigma-Aldrich | L9060-250EA | Decontamination by wiping off surfaces (wet wipes) |
| DNA LoBind Tubes 1.5mL | Eppendorf | 22431021 | Used for collection of cDNA and library post amplifications. |
| DNA LoBind Tubes 2mL | Eppendorf | 22431048 | Used for collection of cDNA before amplification. |
| DNBSEQ OneStep DNB Make Reagent Kit | Complete Genomics | 940-001891-00 | Reagents for DNA nanoball (DNB) preparation and amplification |
| DNBSEQ-T7RS Cleaning Reagent Kit | Complete Genomics | 940-001903-00 | Cartridge for washing after sequencing complete |
| DNBSEQ-T7RS DNB Load Reagent Kit | Complete Genomics | 940-001894-00 | Reagents and plate to load DNBs to flow cell |
| DNBSEQ-T7RS Sequencing Flow Cell | Complete Genomics | 940-001902-00 | 1 lane/flow cell |
| DNBSEQ-T7RS Stereo-seq Visualization Reagent Kit | Complete Genomics | 940-001893-00 | The cartridge and reagents for sequencing. |
| DNBSEQ-T7RS system | Complete Genomics | This stereo-seq visualization set utilizes DNBSEQ technology. A sequencing run starts with the hybridization of a DNA anchor, then a fluorescent probe is attached to the DNA Nanoball (DNB) using combinatorial probe anchor sequencing (cPAS) chemistry. Finally, the high-resolution imaging system captures the fluorescent signal. After digital processing of the optical signal, the sequencer generates high-quality and accurate sequencing information. | |
| Dust-Off Compressed Air | Matin | M-6318 | Important to use this brand or one with similar propellant |
| Edge-Rite Microtome Blades | Thermo Fisher Scientific / Invitrogen | 4280L | Histology - Used when sectioning samples |
| Explosion proof Freezer | Various | N/A | To chill methanol before and during methanol fixation step. |
| Histology Brushes | Various | N/A | Histology - Used when sectioning samples |
| Hydrochloric acid | Sigma-Aldrich | 2104-50ML | For diluting premuliablization enzyme in 0.01 N HCl (pH 2). Standard stock concentration for HCl solution is 0.1 N. |
| Imaging System | various | N/A | GC Stomics Imager, Lecia, Evo Revolution |
| Invitrogen RNaseZap RNase Decontamination Solution | Thermo Fisher Scientific / Invitrogen | AM9780 | Cleaning workstation removing RNAse contamination |
| Kimtech Delicate Task Wipers | Kimtech | 34155 | To dry benches, equipment, pipettes, etc. |
| Labcoat | Various | N/A | Personal Protection Equipment (PPE) |
| Lens Paper | Thermo Fisher Scientific / Invitrogen | 11-997 | Various works for drying slides without sheading |
| Lookout DNA Erase Wipes | Sigma-Aldrich | L9060 | Cleaning workstation removing PCR contamination |
| Magentic Separation Rack 5uL - 0.2 | Permagen Labware | MSRLV08 | For bead sepration in PCR tubes |
| Magjet Rack 12x1.5 mL | Thermo Fisher Scientific / Invitrogen | MR02 | For bead sepration in 1.5 mL tubes |
| Manual Rotary Microtome | Leica | RM2235 | Histology - Used when sectioning samples |
| Methonal ≥99.9%, suitable for immunofluorescence, HPLC | Sigma-Aldrich | 34860 | For fixation (Needs to be HPLC Grade) |
| Micro-dissecting Forceps | Sigma-Aldrich | F4142-1EA | Histology - Used when sectioning samples |
| Micropipette | Various | N/A | |
| Molecular Grade Nuclease Free Water Non-DPEC Treated Water | Various | N/A | Dilutions and elution of beads |
| Nitrile Gloves | Various | N/A | PPE |
| Oven | Various | N/A | To bake chip |
| PCR 0.2 mL Tubes with attached caps | Various | N/A | For PCR applications |
| PCR covers | Various | N/A | Replacement if adhesive on provided cover is weak |
| PCR Hood & Workstation | Mystaire | MY-PCR24-010 | For pre-PCR (RNA to cDNA) steps |
| pH Meter | Various | N/A | pH Meter to confirm HCl pH. |
| Poly-L-Lysine solution | Sigma-Aldrich | A005-C | Optional to increase tissue adhesion to chip |
| Qubit 4 Fluorometer | Thermo Fisher Scientific / Invitrogen | Q33226 | Insuterment; DNA quantiy measurement |
| Qubit dsDNA Quantification Assay Kits | Thermo Fisher Scientific / Invitrogen | Q32851 | For measuring cDNA and library quantifications |
| Qubit ssDNA Assay Kit | Thermo Fisher Scientific / Invitrogen | Q10212 | ssDNA staining (Only dye used) |
| RNAse-free Ice Block | Various | N/A | Histology - Used when sectioning samples to hydrate blocks before sectioning |
| RNaseZap RNase Decontamination Solution | Thermo Fisher Scientific / Invitrogen | AM9782 | A surface decontamination solution that destroys RNAses |
| RNeasy FFPE Kit | Qiagen | 73504 & 19093 | For RNA Extraction & DV200 measurement |
| Sculpt Ultra White Resin | Siraya Tech | N/A | High-temperature resistant UV curable resin |
| Stereo-seq 16 Barcode Library Preparation Kit | STOmics / Complete Genomics | 111KL160 | For constructing Stereo-seq OMNI FFPE Library |
| Stereo-seq FFPE Accessory Kit | STOmics / Complete Genomics | 310AK002 | Spatial OMNI accessory parts of 211SN114 kit |
| Stereo-seq N-chip Slide (1cm x 1cm) | STOmics / Complete Genomics | 210CN114 | Spatial OMNI chip part of 211SN114 kit |
| Stereo-seq PCR Adaptor | STOmics / Complete Genomics | 301AUX001 | For PCR hybridization steps modified to fit three similar to PCRmax Situ Hybridization Adapter |
| Stereo-seq Transcriptomics N Kit | STOmics / Complete Genomics | 211KN114 | Spatial OMNI regent part of 211SN114 kit |
| Surgical Design General Purpose Industrial Razor Blade | Thermo Fisher Scientific / Invitrogen | 13-812-236 | Histology - Used when sectioning samples |
| TE Buffer Solution pH 8.0 | Sigma-Aldrich | 8890 | For eluting cDNA and libraries |
| Thermal Cyclers | BioRad / various | 1861096 /12015392 / various | PCR Machine with manual lid or deep well options (T100 Thermal Cycler / PTC Tempo Deepwell Thermal Cycler etc) |
| Thermo Scientific Orion All-in-One pH Buffer Kits | Thermo Fisher Scientific / Invitrogen | 13-624-500 | |
| Tissue Flotation Bath - Digital XH-1003 | Premiere | NC0779538 | Histology - Used when sectioning samples |
| UV-protective Eyewear | Various | N/A | For safety during UV curing |
| Xylenes | Sigma-Aldrich | 247642 | For deparaffinization |