Cyclic Immunofluorescence
The most widespread method for spatial proteomics is cyclical staining. Instruments that utilize this method, such as the Phenocycler by Akoya Biosciences, Comet by Lunaphore, MACSima by Miltenyi Biotec, CellScape by Canopy Biosciences or Cell DIVE by Leica Microsystems, are integrated stainers and fluorescence imagers. They apply a nuclei marker (e. g. DAPI) plus a set of 3 or 4 additional antibodies conjugated to a standard set of fluorophores and capture this low-plex immunofluorescence image in a first cycle. Then, they wash off the applied antibodies and apply a second set antibodies conjugated to the identical set of fluorophores and capture a second low-plex image. This procedure can be repeated multiple cycles to obtain a higher and higher plexity. The nuclei marker can be imaged each round or at least intermittently to serve as a reference for later aligning and fusing the set of low-plexes into a single high-plex image. An advantage of this method is the high plexity that can be achieved. A drawback is the long scan duration, which can take many hours up to a day.
Manual Sequential Immunofluorescence
When no such instrument is available, an alternative is to manually stain, scan and wash multiple cycles of antibodies and use an image registration software to eventually merge the scans together into a high-plex. Essentially any fluorescence microscope or scanner, such as the Zeiss AxioScan or Olympus VS200, can be used in this workflow.
High-plex Spectral Imaging
An alternative imaging method is used by instruments such as Orion by RareCyte (up to 20 plex) or PhenoImager by Akoya Biosciences (up to 6 plex). They are equipped with a multispectral optical system that can resolve a larger number of fluorescence channels and therefore no cyclic procedure is required. Instead, all antibodies can be captured in parallel.
A drawback is that the achievable plexity of this method is more limited compared to cyclical IF. An advantage is that the scan durations are much higher and no co-registration of cycles, which brings with it the risk of introducing alignment errors, is required.
Co-Alignment of IHC Serial Sections
To a limited degree, spatial proteomics is also possible using brightfield immunohistochemistry. Resorting to this method has the advantage that IHC antibodies may already be established in many labs and the risk of wasting expensive anti-bodies or obtaining unreliable signals is low. Restaining the same tissue section is possible but rare. More predominant is the staining of serial sections. After all sections are digitized, they need to be aligned to one another. The center section can serve as a reference and adjacent sections are non-rigidly rotated, translated and morphed to obtain a resulting image that can be overlaid on top of the reference. Nonetheless, co-expression with a cellular resolution is hardly feasible in this approach since the same cell is likely not visible in both sections. The downstream image analysis should consider this limitation and evaluate co-expression patterns on a local but slightly coarser level. The cell-cell connections analysis or grid analysis methods outlined below are eligible methods in this scenario.
Example Use Case: Biomarker Discovery
Before diving into the intricate details of the bioinformatic analysis of spatial proteomics data, this paragraph shall provide an example of where this technology is used. Researchers at the Max Delbrück Center for Molecular Medicine (MDC) in Berlin analyze tissue from a cohort of patients with head and neck squamous cell carcinoma (HNSCC) who have been treated with PD-L1 immune checkpoint therapy. All figures in this article are created with MIKAIA studio, developed by Fraunhofer IIS [1], on this cohort.
HNSCC arises in the laryngeal, pharyngeal, or oral cavities, and the main factors contributing to the development of these tumors are HPV infection and/or alcohol consumption and smoking. HPV-positive patients generally have a better prognosis due to immune system activation triggered by the infection itself. Patients with recurrent and metastatic tumors are treated with PD-L1 immunotherapy, provided they demonstrate positive PD-L1 expression. However, this therapy is only effective in 15 to 20 % of patients. Due to the lack of alternative therapies, some patients without PD-L1 expression also receive this form of immunotherapy.
The goal now is to find alternative therapies as well as better prognostic markers for identifying patients who will benefit from immunotherapy. By locating and phenotyping the different cell types, such as immune and cells, their distribution, abundance and interaction can be quantitatively measured. Cells of the same type can behave differently depending on their cellular communication within distinct neighborhoods [5]. This information is then used to compare patients who respond to therapy with those who do not.
AI Cell Segmentation
Regardless of which imaging method is used, the primary analysis steps are similar. Imaged field of views are stitched and illumination corrected, typically, this is done already by the scanning application. In case of fluorescence microscopy, autofluorescence should then be mea-sured and deducted from the marker channel images. For cell segmentation, AI based approaches such as CellPose, StarDist or Mesmer are superior over computer vision-based methods and their use is highly recommended despite the longer computation times since all secondary analyses are based and rely on robustly detected cells. If a membrane stain (or cocktail of membrane stains) is available, this should be fed into the AI to delineate the true cell boundary. The DNA marker (e. g. DAPI or Hoechst) is additionally fed into the AI and serves as a seed for locating cells. While CellPose will find either the nuclei or the membrane contour, other AIs such as Mesmer can identify both in a single run. If no membrane stain is available, it is common to estimate the cell contour by dilating nuclei by a fixed radius.
In case of brightfield IHC either cell segmentation AIs trained on IHC can be used. An alternative method is to deconvolve both stains (e. g. hematoxylin and DAB) and convert these channels into the optical density (OD) color space, which produces images that look similar to fluorescent images as pixels with a high (low) stain intensity map to brighter (darker) pixels in the resulting OD grey level image (Fig. 1).
