Spatial Analysis
SPATIAL ANALYSIS
Product Overview
Particularly useful to those involved in the immune-oncology field, the Spatial Analysis module offers a suite of subsequent analysis tools which can be used to identify proximity and relative spatial distribution of objects, cells, and/or features across single tissues or serial sections. This module is embedded within the HALO platform and can be used in conjunction with any of our cell-based analysis modules for brightfield or fluorescence.
Nearest Neighbor Analysis
Determine the average distance and number of unique neighbors between any two cell or object populations. The example below depicts pan cytokeratin positive cells (blue) and CD4 positive cells (red). Grey lines connect each CD4 positive cell to the nearest pan cytokeratin positive cell.
Proximity Analysis
Calculate the number of objects or cells within a certain distance of another object or cell. In the example below, the distance is set to 30 microns. CD8 positive cells within 30 microns of a pan cytokeratin positive cell (blue) are labeled green and CD8 positive cells greater than 30 microns from pan cytokeratin positive cell are labeled purple. A corresponding proximity histogram is automatically generated.
Infiltration Analysis
Determine the number of objects or cells within a set range of an annotated region of interest. Depicted below is a whole slide image stained with CD8. The tumor boundary has been defined/annotated in green. The infiltration analysis tool defines the invasive margin inside of tumor (yellow and red) and the invasive margin outside of the tumor (blue and purple) automatically. In this example, the distance is set to 500 microns around the annotated tumor boundary. CD8 positive cells are quantified within the margin. A histogram is automatically generated to reflect CD8+ cell density inside the tumor boundary (-1 to -500), at the tumor boundary (0), and outside the tumor boundary (+1 to +500) as shown in the image below.
Density Heatmaps
The density heatmap spatial analysis algorithm measures the density of a selected cell population from your object data within a certain radius. The scaling and colors of the markup are customizable. Reported in the summary data, the entire tissue area is analyzed and all cells of the desired population are counted. The average cell population and the average cell population density is calculated within the user-defined radius, as well as the minimum and maximum densities.
The tissue below is stained with an immune cell marker (red stain) and a tumor marker (DAB stain). The image was analyzed with the Multiplex IHC module to detect positivity and produce object data. The density heatmap analysis was used with a radius of 25 microns, measuring the density of immune cell positivity across the whole slide. The markup image, with adjustable transparency, depicts areas of higher density in red and orange, moderate density in yellow, and areas of lower density in green and blue.
The Spatial Analysis module integrates seamlessly into the HALO™ platform and is compatible with a number of file formats.
Contact info@indicalab.com for product demonstration and pricing information or upload some images for a free trial.
SPATIAL ANALYSIS
Product Overview
Particularly useful to those involved in the immune-oncology field, the Spatial Analysis module offers a suite of subsequent analysis tools which can be used to identify proximity and relative spatial distribution of objects, cells, and/or features across single tissues or serial sections. This module is embedded within the HALO platform and can be used in conjunction with any of our cell-based analysis modules for brightfield or fluorescence.
Nearest Neighbor Analysis
Determine the average distance and number of unique neighbors between any two cell or object populations. The example below depicts pan cytokeratin positive cells (blue) and CD4 positive cells (red). Grey lines connect each CD4 positive cell to the nearest pan cytokeratin positive cell.
Proximity Analysis
Calculate the number of objects or cells within a certain distance of another object or cell. In the example below, the distance is set to 30 microns. CD8 positive cells within 30 microns of a pan cytokeratin positive cell (blue) are labeled green and CD8 positive cells greater than 30 microns from pan cytokeratin positive cell are labeled purple. A corresponding proximity histogram is automatically generated.
Infiltration Analysis
Determine the number of objects or cells within a set range of an annotated region of interest. Depicted below is a whole slide image stained with CD8. The tumor boundary has been defined/annotated in green. The infiltration analysis tool defines the invasive margin inside of tumor (yellow and red) and the invasive margin outside of the tumor (blue and purple) automatically. In this example, the distance is set to 500 microns around the annotated tumor boundary. CD8 positive cells are quantified within the margin. A histogram is automatically generated to reflect CD8+ cell density inside the tumor boundary (-1 to -500), at the tumor boundary (0), and outside the tumor boundary (+1 to +500) as shown in the image below.
Density Heatmaps
The density heatmap spatial analysis algorithm measures the density of a selected cell population from your object data within a certain radius. The scaling and colors of the markup are customizable. Reported in the summary data, the entire tissue area is analyzed and all cells of the desired population are counted. The average cell population and the average cell population density is calculated within the user-defined radius, as well as the minimum and maximum densities.
The tissue below is stained with an immune cell marker (red stain) and a tumor marker (DAB stain). The image was analyzed with the Multiplex IHC module to detect positivity and produce object data. The density heatmap analysis was used with a radius of 25 microns, measuring the density of immune cell positivity across the whole slide. The markup image, with adjustable transparency, depicts areas of higher density in red and orange, moderate density in yellow, and areas of lower density in green and blue.
HALO is compatible with a broad spectrum of image and digital slide formats. Yours not on the list? Email us your requirements.
Non-proprietary (JPG, TIF)
Nikon (ND2)
3D Histech (MRXS)
Perkin Elmer (QPTIFF, component TIFF)
Olympus (VSI)
Hamamatsu (NDPI, NDPIS)
Aperio (SVS, AFI)
Zeiss (CZI)
Leica (SCN, LIF)
Philips (Requires Philips IMS)
Here are a few publications that cite the use of our HALO Spatial Analysis module. Your publication not on the list? Drop us an email to let us know about it!
| Title | Authors | Year | Journal | Application | HALO Modules |
|---|---|---|---|---|---|
| Neoadjuvant anti-PD-1 immunotherapy promotes a survival benefit with intratumoral and systemic immune responses in recurrent glioblastoma | Cloughesy TF, Mochizuki AY, Orpilla JR, Hugo W, Lee AH, Davidson TB, Wang AC, Ellingson BM, Rytlewski JA, Sanders CM, Kawaguchi ES, Du L, Li G, Yong WH, Gaffey SC, Cohen AL, Mellinghoff IK, Lee EQ, Reardon DA, O’Brien BJ, Butowski NA, Nghiemphu PL, Clarke JL, Arrillaga-Romany IC, Colman H, Kaley TJ, de Groot JF, Liau LM, Wen PY, Prins RM | 2019 | Nature Medicine | Oncology, Immuno-oncology | Spatial Analysis, Highplex FL |
| Multidimensional, quantitative assessment of PD-1/PD-L1 expression in patients with Merkel cell carcinoma and association with response to pembrolizumab | Giraldo NA, Nguyen P, Engle EL, Kaunitz GJ, Cottrell TR, Berry S, Green B, Soni A, Cuda JD, Stein JE, Sunshine JC, Succaria F, Xu H, Ogurtsova A, Danilova L, Church CD, Miller NJ, Fling S, Lundgren L, Ramchurren N, Yearley JH, Lipson EJ, Cheever M, Anders RA, Nghiem PT, Topalian SL, Taube JM | 2018 | Journal for ImmunoTherapy of Cancer | Oncology, Immuno-oncology | Spatial Analysis, Serial Section |
| Precision immunoprofiling by image analysis and artificial intelligence | Koelzer VH, Sirinukunwattana K, Rittscher J, Mertz KD | 2019 | Vichows Archiv | Review | Classifier, Multiplex IHC, Spatial Analysis |
| Spatial immune profiling of the colorectal tumor microenvironment predicts good outcome in stage II patients | Nearchou IP, Gwyther BM, Georgiakakis ECT, Gavriel CG, Kajiwara Y, Ueno H, Harrison DJ, Caie PD | 2020 | Digital Medicine | Immuno-oncology | Classifier, Area Quantification, Spatial Analysis |
| Locally confined IFNɣ production by CD4+ T cells provides niches for murine cytomegalovirus 4 replication in the salivary gland | Oderbolz J, Zangger N, Zimmerman L, Sandu I, Starruß J, Graw F, Oxenius A | 2021 | bioRxiv | Classifier, Spatial Analysis, Highplex FL | |
| Genetic alterations and expression characteristics of ARID1A impact tumor immune contexture and survival in early-onset gastric cancer | Zou J, Qin W, Wang L, Wang Y, Shen J, Xiong W, Yu S, Song S, Ajani JA, Lin S-Y, Mills GB, Yuan X, Chen J, Peng G | 2020 | American Journal of Cancer Research | Immuno-oncology | Spatial Analysis, Highplex FL |
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