microMS: A Python Platform for Image-Guided Mass Spectrometry Profiling

Abstract

Image-guided mass spectrometry (MS) profiling provides a facile framework for analyzing samples ranging from single cells to tissue sections. The fundamental workflow utilizes a whole-slide microscopy image to select targets of interest, determine their spatial locations, and subsequently perform MS analysis at those locations. Improving upon prior reported methodology, a software package was developed for working with microscopy images. microMS, for microscopy-guided mass spectrometry, allows the user to select and profile diverse samples using a variety of target patterns and mass analyzers. Written in Python, the program provides an intuitive graphical user interface to simplify image-guided MS for novice users. The class hierarchy of instrument interactions permits integration of new MS systems while retaining the feature-rich image analysis framework. microMS is a versatile platform for performing targeted profiling experiments using a series of mass spectrometers. The flexibility in mass analyzers greatly simplifies serial analyses of the same targets by different instruments. The current capabilities of microMS are presented, and its application for off-line analysis of single cells on three distinct instruments is demonstrated. The software has been made freely available for research purposes. Graphical Abstract ᅟ.

Keywords: Image-guided mass spectrometry; MALDI; SIMS; Single cell analysis; Software.

Figure 1

Partial unified modeling language diagram of the microMS class structure. Each experiment is contained in a microMSModel object, consisting of a list of targets (blobList), a microscopy image (slideWrapper), and an instrument mapper (coordinateMapper). The coordinateMapper defines the set of instructions required for each inheriting class. Four concrete instrument implementations are provided with microMS.

Figure 2

Overview of blob finding with microMS. (A) From an input image, the pixel intensity is filtered by a threshold intensity. (B) Pixels above the threshold are grouped with neighboring pixels to generate putative blobs. Each group of pixels is evaluated for its size (in pixels) and circularity and stored within a list, shown as a table in (C). The area (A) and perimeter (P) are directly measured from the threshold image. †The effective radius is calculated as $\sqrt{\raisebox{1ex}{$A$}\!\left/ \!\raisebox{-1ex}{$\pi $}\right.}$. ‡The circularity is calculated as $\frac{4\pi A}{P^2}$.

Figure 3

Population-level filtering through the histogram window. (A) A population of found blobs may be filtered by size, circularity, minimum pairwise distance, or fluorescence intensity. (B and C) The histogram can be divided into a low-pass, high-pass, or single interval, with the appropriate blobs dynamically colored in the microscope image.

Figure 4

Schematic of fiducial training. (A) The input image includes several fiducial points, such as etched x marks on the glass slide. (B) An initial attempt at registration with labels of the nearest named coordinate for each fiducial. Fiducials are shown in blue, except the point with the worst fiducial localization error, which is in red. A set of predicted locations in yellow are also be displayed. (C) After removing and retraining the worst fiducial, the next worst fiducial is dynamically highlighted.

Figure 5

Determination of target localization errors. (A) Target locations (green) are marked around an image of an etched x mark. The sample is then coated with a thin layer of MALDI matrix and analyzed by optically-guided MS to generate desorption craters in the matrix. (B) The location of resulting desorption events (red) are determined by optical microscopy. (C) Image registration of panels A and B allows the direct mapping of requested target locations onto the desorption marks. Overlap is shown in yellow. The distance between these positions is the target localization error of the registration set. The effect of various parameters may be assessed simultaneously including multiple training sets, location on slide, or size of microprobe, as shown here. A three-way linear ANOVA demonstrated that while the specific fiducial training set significantly affected accuracy (p ≪ 0.05; (D), the location on the slide (p = 0.6; (E) and spot size (p = 0.3; not shown) did not.

Figure 6

Sequential analysis of the same cell with two separate MS systems. Once a cell has been located in the optical image (top), its location remains fixed through multiple analyses, allowing two instruments to probe the same set of selected cells. (A) MALDI-TOF MS (middle) of a cerebellum-derived cell followed by MALDI-FT-ICR MS (bottom). MALDI-TOF MS provides high throughput screening of thousands of cells to highlight rare or representative individuals. Here, FT-ICR MS provides high mass resolution and high mass accuracy for unequivocal elemental composition of selected cellular contents. (B) SIMS profiling (middle) followed by MALDI-TOF MS (bottom) with a DHB-coated, SCN-derived cell. SIMS provides information on small molecule compounds while MALDI-TOF MS effectively detects larger species, such as lipid dimers and peptides. The inset demonstrates some overlap of intact lipid coverage from each modality.