Workflow

Bring the advantage of superior reproducibility and time-saving automated single cell partitioning to your bench, at a significantly lower cost.

How Microscoop^® Works

Region of Interest (ROI) Selection and Image Analysis

Cells (Fixed, Frozen) or Tissue (FFPE, Fresh Frozen, PFA/Methanol fixed) are stained with a marker that the researcher uses to predefine areas for deeper proteomic analysis. This can be stained with an immunofluorescent marker, multiple markers or other anatomical marker of interest that can be identified via microscopy. One can define these regions based on size, shape, location, distance, and intensity. The user places the stained sample on the Microscoop stage and the sample is imaged. These ROI’s are identified manually within one field of view (FOV) and converted by the Microscoop Autoscoop Software to an image mask, a binary parameter, that tells the system which specific locations to target for whole proteome analysis. The image mask is automatically applied to the entire sample. The ROI’s are the “blueprint” by which the 2-photon laser uses in the next step to target the specific locations of interest.

Patterned
Photo-Biotinylation

The Synlight Rich kit, containing a photobiotinylation reagent, is applied to the entire sample. A femtosecond light source is controlled mechatronically is used to illuminate the region of interest one pixel at a time. This patterned illumination triggers targeted protein photo-biotinlyation with high spatial precision through the reactions of light-sensitive probes of the Synlight-Rich™ Kit. This patterned photolabeling is repeated for hundreds to thousands of FOVs automatically. The number of photolabeling events is dictated by the size and quantity of the ROI’s. The intention is to aggregate all ROI protein material together to ensure there is sufficient protein to go through the protein extraction and input to downstream mass spectrometry or alternative downstream proteomic readouts. This entire step is done automatically and requires no user intervention until all pixels are labeled by the laser across the FOV’s of interest.

Protein Extraction

The entire sample contents are collected from the slide or chamber. Materials from multiple slides or chambers can be pooled together to increase the total protein content. The Synpull Kit is used to carry out the next steps of extraction. Using the kit, the samples are lysed, biotinylated proteins are enriched and purified by immunoprecipitation, and proteins digested into peptides for proteomic analysis.

Proteomic Identification and Analysis

The collected peptides can be lyophilized for downstream proteomic analysis. Typically, an unbiased proteomic analysis of the protein lysate collected from the Synpull Assay is performed on a mass spectrometer (LC-MS/MS). Proteomes of both the photo-labeled and unlabeled (control) samples are obtained. By comparing the proteomic makeup of the control and photolabeled samples, a location-specific proteome is obtained with high sensitivity and, high specificity with high spatial precision. Validation can be done by colocalization of immunostaining, subsequent follow-on mass spec experiments or additional functional assays. Unique analyses like differential protein expression, treatment/control, disease/normal, and post translational modifications can also be carried out with the output of the Microscoop.

Validated with Success on multiple, easily accessible, LC/MS instruments

Thermo Fisher

Orbitrap Fusion Lumos
Orbitrap Eclipse
Orbitrap Exploris 480
Orbitrap Exploris 240
Orbitrap Ascend
Orbitrap Astral

Bruker

timsTOF Ultra 2
timsTOF Ultra
timsTOF SCP
timsTOF HT
timsTOF Pro 2

SciEx

ZenoTOF 7600

Alternative methods
and the challenges with them

For Antibody-Based Spatial Proteomics

Approach Dependence: Traditional methods require a hypothesis-driven approach and prior knowledge of antibody targets, limiting exploratory research.
Complex Workflow: Time-consuming and costly processes, including antibody titration, pooling, and conjugation, hinder efficiency.
Limited Multiplexing: Constrained ability to detect multiple proteins simultaneously reduces experimental scope and depth.
Data Extraction Challenges: Software and imaging algorithm limitations make it difficult to fully resolve and interpret antibody localization.

For Proximity Labeling Techniques

Model Limitations: Relies on cloning cells and growing them in vitro or in mouse models, excluding direct human applications.
Localization Challenges: Cannot resolve gene activity across multiple locations, such as cytoplasm versus nucleus.
Labor-Intensive Workflows: Manual processes require significant time and effort, reducing efficiency.
Specificity Issues: High levels of non-specific protein binding compromise data accuracy.

For Laser Capture Microdissection for Single/Subcellular Proteomic Analysis

Resolution Constraints: XY-resolution (~200 nm) and Z-resolution (3–20 μm) are insufficient for small, specific structures like nucleoli, stress granules, and primary cilia.
Z-Axis Limitations: Low Z-axis resolution increases non-specificity for small targets, compromising accuracy.
Throughput Challenges: Limited scalability, with a maximum of ~100 cells isolated per hour even after optimization.
Application Restrictions: Current resolution and throughput hinder use in high-precision and large-scale studies.