DOI: 10.5061/dryad.ksn02v7b1
title: Spatial profiling of benign and malignant melanocytic tumors via RNA-SMI (CosMx)

description: Melanoma clinical outcomes emerge from incompletely understood genetic
 mechanisms operating within the tumor and its microenvironment. Here, we
 utilized single-cell RNA-based spatial molecular imaging (RNA-SMI) in
 patient-derived archival tumors to reveal clinically relevant markers of
 malignancy progression and prognosis. We examined spatial gene expression
 of 203,472 cells inside benign and malignant melanocytic neoplasms,
 including melanocytic nevi, primary invasive and metastatic melanomas.
 Algorithmic cell clustering paired with intratumoral comparative
 2D-analyses visualized synergistic, spatial gene signatures linking
 cellular proliferation, metabolism, and malignancy, validated by protein
 expression. Metastatic niches included upregulation of CDK2 and FABP5,
 which independently predicted poor clinical outcome in 473 melanoma
 patients via Cox regression analysis. More generally, our work
 demonstrates a framework for applying single-cell RNA-SMI technology
 toward identifying gene regulatory landscapes pertinent to cancer
 progression and patient survival.
description: <strong>NanoString® CosMx™ RNA Spatial Molecular Imaging
 (SMI)</strong> The protocol used for NanoString®
 CosMx™ RNA Spatial Molecular Imaging (SMI) was based on the method
 previously described by He et al. (High-plex imaging of RNA and proteins
 at subcellular resolution in fixed tissue by spatial molecular imaging.
 <em>Nat Biotechnol</em> 40, 1794-1806. 2022). 5-μm
 formalin-fixed, paraffin-embedded (FFPE) tissue sections were mounted on
 VWR Superfrost Plus Micro slides (cat# 48311-703) and baked at 60°C
 overnight to improve tissue-slide adherence. The slides were prepared for
 in-situ hybridization (ISH) by heat-induced epitope retrieval (HIER) at
 100°C for 15 min using ER1 epitope retrieval buffer (Leica Biosystems
 product, citrate-based, pH 6.0). Following HIER, the tissues were digested
 with 3 µg/ml Proteinase K diluted in ACD Protease Plus (Advanced Cell
 Diagnostics, Inc.) at 40°C for 30 minutes. Slides were washed twice with
 diethyl pyrocarbonate (DEPC)-treated water (DEPC H2O) and incubated in
 0.0005% diluted fiducials (Bangs Laboratory, Inc.) in 2X SSCT (2X saline
 sodium citrate, 0.001% Tween-20) solution for 5 min at room temperature in
 the dark. Excess fiducials were rinsed from the slides with 1X phosphate
 buffered saline (PBS) and tissue sections were fixed with 10% neutral
 buffered formalin (NBF) for 5 min at room temperature. Fixed samples were
 rinsed twice with Tris-glycine buffer (0.1M glycine, 0.1M Tris-base in
 DEPC H2O) and once with 1X PBS for 5 min each before blocking with 100 mM
 N-succinimidyl (acetylthio) acetate (NHS-acetate, ThermoFisher) in
 NHS-acetate buffer (0.1M NaP, 0.1% Tween PH 8 in DEPC H2O) for 15 min at
 room temperature. The sections were then rinsed with 2X saline sodium
 citrate (SSC) for 5 min and an Adhesive SecureSeal Hybridization Chamber
 (Grace Bio-Labs) was placed over the tissue.
 NanoString® ISH probes were prepared by incubation at 95°C for 2
 min and placed on ice, and the ISH probe mix (1 nM 980 plex ISH probes, 1
 nM custom probes, 10nM Attenuation probes, 1X Buffer R, 0.1 U/μL
 SUPERase•In™ [Thermofisher] in DEPC H2O) was pipetted into the
 hybridization chamber. The genes targeted for each analysis are listed in
 Data S3 and Data S7. The hybridization chamber was sealed to prevent
 evaporation, and hybridization was performed at 37°C overnight. Tissue
 sections were rinsed of excess probes in 2X SSCT for 1 min and washed
 twice in 50% formamide (VWR) in 2X SSC at 37°C for 25 min, then twice with
 2X SSC for 2 min at room temperature and blocked with 100 mM NHS-acetate
 in the dark for 15 min. A custom-made flow cell was affixed to the slide
 in preparation for loading onto the CosMx SMI instrument.
 RNA target readout was performed as described in He et al.
 (High-plex imaging of RNA and proteins at subcellular resolution in fixed
 tissue by spatial molecular imaging. <em>Nat Biotechnol</em>
 40, 1794-1806. 2022). Briefly, the assembled flow cell was loaded onto the
 CosMx SMI instrument and Reporter Wash Buffer was flowed to remove air
 bubbles. A preview scan of the entire flow cell was taken, and diagnostic
 areas of each tumor were targeted using 0.7 x 0.9 mm fields of view (FOVs,
 21 to 25 per slide as listed in Fig. S2, Data S4) to match regions of
 interest identified by H&E staining of an adjacent serial section.
 RNA readout began by flowing 100 μl of Reporter Pool 1 into the flow cell
 and incubation for 15 min. Reporter Wash Buffer (1 mL) was flowed to wash
 unbound reporter probes, and Imaging Buffer was added to the flow cell for
 imaging. Nine Z-stack images (0.8 μm step size) for each FOV were
 acquired. Photocleavable linkers on the fluorophores of the reporter
 probes were released by UV illumination and washed with Strip Wash buffer.
 The fluidic and imaging procedure was repeated for the 16 reporter pools,
 and the 16 rounds of reporter hybridization-imaging were repeated multiple
 times to increase RNA detection sensitivity.<br>After RNA readout,
 tissue samples were incubated with a 4-fluorophore-conjugated antibody
 cocktail against CD298/B2M (488 nm), S100b/PMEL17 (532 nm), CD45 (594 nm),
 and CD3 (647 nm) proteins and DAPI stain in the CosMx SMI instrument for 2
 h. After unbound antibodies and DAPI stain were washed with Reporter Wash
 Buffer, Imaging Buffer was added to the flow cell and nine Z-stack images
 for the 5 channels (4 antibodies and DAPI) were captured. 
 <strong>Cell segmentation of CosMx SMI
 data</strong> CosMx data underwent cell
 segmentation according to methods described previously (He<em> et
 al.</em>, High-plex imaging of RNA and proteins at subcellular
 resolution in fixed tissue by spatial molecular imaging. <em>Nat
 Biotechnol</em> 40, 1794-1806. 2022). This segmentation process
 employed a machine learning algorithm (references; M. Pachitariu, C.
 Stringer, Cellpose 2.0: how to train your own model. <em>Nat
 Methods</em> 19, 1634-1641. 2022., and C. Stringer, T. Wang, M.
 Michaelos, M. Pachitariu, Cellpose: a generalist algorithm for cellular
 segmentation. <em>Nat Methods </em>18, 100-106. 2021.) that
 utilizes z-stack images of immunostaining and DAPI to delineate cell
 boundaries and subsequently assign transcripts to specific cell locations
 and subcellular compartments. The resulting transcript profiles of
 individual cells were created by integrating the target transcript's
 location with the boundaries established during cell
 segmentation.
description: # Spatial Profiling of Benign and Malignant Melanocytic Tumors via RNA-SMI
 (CosMx)
 [https://doi.org/10.5061/dryad.ksn02v7b1](https://doi.org/10.5061/dryad.ksn02v7b1) ## Description of the data and file structure "Slide_1.zip", "Slide_2.zip", "Slide_3.zip", "Slide_4.zip":  Each .zip folder contains the RNA-SMI data pertaining to Slides 1-3, which examined 203,472 cells amongst ten melanocytic tumors (exact tumor identity per slide is outlined in fig. S1, with further slide information regarding microscopic field of view distribution outlined in fig. S2). Slide 4 contains the RNA-SMI data examining 84,312 cells including the nevus-melanoma mixed tumor featured in fig. 4 as well as four other melanocytic tumors (one melanoma, one cutaneous metastasis, and two nevi). Specifically, each .zip folder contains the following files and folders: **Files:** Transcript file (tx_file.csv), which contains columns for: * —fov (Field Of View (FOV) where transcript is located) * —cell_ID (unique identifier of cell with a FOV; together, the “fov” and “cell_ID” columns are able to define a unique identifier for each cell in the entire sample. Note, transcripts without an assigned cell have a value of 0) * —x_global_px (see “x_local_px” description below; global position entails the relative transcript position within the large sample reference frame) * —y_global_px (as “x_global_px” but for y dimension) * —x_local_px (x position of transcript within FOV, measured in pixels. Note, to convert to microns, multiply pixel value by 0.18 um per pixel) * —y_local_px (same as “x_local_px” but for y dimension) * —target (HUGO gene symbol of target) * —CellComp (nuclear, membrane, or cytoplasmic of subcellular compartment where transcript was detected via cell segmentation algorithm; note, “0” denotes extracellular as mentioned above) Cell polygons file (polygons.csv), which contains simple polygon descriptions of cell boundaries in columns: * —fov (Field Of View [FOV] where transcript is located) * —cell_ID (unique identifier of cell with a FOV; together, the “fov” and “cell_ID” columns are able to define a unique identifier for each cell in the entire sample) * —x_local_px (x position within FOV, measured in pixels; note, to convert to microns, multiply pixel value by 0.18 um per pixel) * —y_local_px (same as “x_local_px” but for y dimension) * —x_global_px (relative x position of the ROV, measured in pixels. Note, to convert to microns, multiply pixel value by 0.18 um per pixel) * —y_global_px (as “x_global_px” but for y dimension) Cell expression file (exprMat_file.csv), which contains gene expression counts per cell per gene  in columns: * —fov (Field Of View [FOV] where transcript is located) * —cell_ID (unique identifier of cell with a FOV; together, the “fov” and “cell_ID” columns are able to define a unique identifier for a cell in the entire sample; note, transcripts without an assigned cell have a value of 0) * —Alphabetical columns of genes targets (number of transcripts detected per gene target per cell) * —Columns of negative probes (probes that do not match any sequence within the transcriptome, which can can be used to assess background levels) Cell metadata file (metadata_file.csv), which contains metadata per cell in columns: * —fov (Field Of View [FOV] where transcript is located) * —cell_ID (unique identifier of cell with a FOV; together, the “fov” and “cell_ID” columns are able to define a unique identifier for each cell in the entire sample; note, transcripts without an assigned cell have a value of 0) * —Area (total pixels per given cell) * —Aspect ratio (width divided by height) * —CenterX_global_px (see “CenterX_local_px” description below; global positions describe the relative transcript position within the large sample reference frame) * —CenterY_global_px (as “CenterX_global_px” but for y dimension) * —CenterX_local_px (x position of transcript within the FOV in pixels; the pixel edge length is 180nm. Thus, to convert to microns multiply the pixel value by 0.18 um per pixel) * —CenterY_local_px (as “CenterX_local_px” but for y dimension) * —Width (maximum cell length in x dimension in pixels) * —Height (maximum cell length in y dimension in pixels) * —Mean.MembraneStain (mean fluorescence intensity of a given cell’s membrane stain i.e., CD298) * —Max.MembraneStain (max fluorescence intensity of a given cell’s membrane stain i.e., CD298) * —Mean.S100b.PMEL17 (mean fluorescence intensity of a given cell’s S100B/PMEL17 stain) * —Max.S100b.PMEL17 (max fluorescence intensity of a given cell’s S100B/PMEL17 stain) * —Mean.CD45 (mean fluorescence intensity of a given cell’s CD45 stain) * —Max.CD45 (max fluorescence intensity of a given cell’s CD45 stain) * —Mean.CD3 (mean fluorescence intensity of a given cell’s CD3 stain) * —Max.CD3 (max fluorescence intensity of a given cell’s CD3 stain) * —Mean.DAPI (mean fluorescence intensity of a given cell’s DAPI stain) * —Max.DAPI (max fluorescence intensity of a given cell’s DAPI stain) FOV Positions File (fov_positions_file.csv), which provides each FOV location within the total structure of the sample in columns: * —fov (Field Of View [FOV]) * —x_global_px (relative x position of FOV in pixels; yo convert to microns, multiply the pixel value by 0.18 um per pixel; NB, all FOVs are 5472 x 3648 pixels) * —y_global_px (as “x_global_px” but for y dimension) **Folders:** “CellOverlay”: Contains JPG images of each FOV showing cell boundaries from cell segmentation (shown as cyan lines) as well as DAPI stain in white. “CellComposite”: Contains JPG images of each FOV showing the immunofluorescence from the IHC markers and DAPI used in the SMI experiment "CompartmentLabels”: Contains TIF images that display the subcellular compartment definitions for each FOV determined during cell segmentation. Each compartment type is given a unique number and all pixels determined to be within that compartment type have an intensity value matching that number. “RawMorphologyImages”: Raw TIF image files for each FOV used for cell segmentation and to produce the cellComposite jPG images. “CellLabels”: TIF files for each FOV that displays the cell definitions determined during cell segmentation. Each cell identified is given a unique number (cell_ID) and all pixels determined to be within that cell have an intensity value matching that number. These cell_ID values are shared in the transcript, Cell Expression, and Cell Metadata files. A pixel not assigned to a cell has a “0” value. --- Other files contained in this upload include: * "processed_metadata_Slides_1-3.csv" and "processed_metadata_Slide_4.csv": These files contains processed data and cell-level analyses of Slides 1-3 and Slide 4, using the columns headers described in the “tx_file.csv”, “polygons.csv”, “exprMat_file.csv”, “metadata_file.csv”, “fov_positions_file.csv” files above. * FOVs for Slides 1-3.csv: Contains lesion descriptions of each FOV in Slides 1-3. * FOVs for Slide 4.csv: Contains lesion descriptions of each FOV in Slide 4. * InSituType package materials.zip: This self-contained .zip folder contains that InSituType package that utilized during the "Semi-supervised cell type annotation" data analysis; this package was available on GitHub @ [https://github.com/Nanostring-Biostats/InSituType/tree/main](https://github.com/Nanostring-Biostats/InSituType/tree/main) as of April 10th 2024. --- ## Code/Software Files are compatible with open-source packages such as Seurat in R or Squidpy in Python. More information about how to employ these open-source packages can be found at: Seurat: [https://satijalab.org/seurat/](https://satijalab.org/seurat/) Squidpy: [https://squidpy.readthedocs.io/en/stable/notebooks/tutorials/tutorial\\_nanostring.html](https://squidpy.readthedocs.io/en/stable/notebooks/tutorials/tutorial\\_nanostring.html)