Correlative imaging of trace elements and intact molecular species in a single-tissue sample at the 50 μm scale

Elemental and molecular imaging play a crucial role in understanding disease pathogenesis. To accurately correlate elemental and molecular markers, it is desirable to perform sequential elemental and molecular imaging on a single-tissue section. However, very little is known about the impact of performing these measurements in sequence. In this work, we highlight some of the challenges and successes associated with performing elemental mapping in sequence with mass spectrometry imaging. Specifically, the feasibility of molecular mapping using the mass spectrometry imaging (MSI) techniques matrix-assisted laser desorption ionization (MALDI) and desorption electrospray ionization (DESI) in sequence with the elemental mapping technique particle-induced X-ray emission (PIXE) is explored. Challenges for integration include substrate compatibility, as well as delocalization and spectral changes. We demonstrate that while sequential imaging comes with some compromises, sequential DESI-PIXE imaging is sufficient to correlate sulfur, iron, and lipid markers in a single tissue section at the 50 μm scale

Metabolic profiling stratifies colorectal cancer and reveals adenosylhomocysteinase as a therapeutic target

The genomic landscape of colorectal cancer (CRC) is shaped by inactivating mutations in tumour suppressors such as APC, and oncogenic mutations such as mutant KRAS. Here we used genetically engineered mouse models, and multimodal mass spectrometry-based metabolomics to study the impact of common genetic drivers of CRC on the metabolic landscape of the intestine. We show that untargeted metabolic profiling can be applied to stratify intestinal tissues according to underlying genetic alterations, and use mass spectrometry imaging to identify tumour, stromal and normal adjacent tissues. By identifying ions that drive variation between normal and transformed tissues, we found dysregulation of the methionine cycle to be a hallmark of APC-deficient CRC. Loss of Apc in the mouse intestine was found to be sufficient to drive expression of one of its enzymes, adenosylhomocysteinase (AHCY), which was also found to be transcriptionally upregulated in human CRC. Targeting of AHCY function impaired growth of APC-deficient organoids in vitro, and prevented the characteristic hyperproliferative/crypt progenitor phenotype driven by acute deletion of Apc in vivo, even in the context of mutant Kras. Finally, pharmacological inhibition of AHCY reduced intestinal tumour burden in ApcMin/+ mice indicating its potential as a metabolic drug target in CRC

DEVELOPMENT OF A SYSTEM TO STUDY THE EFFECTS OF HISTONE MUTATIONS AND POST-TRANSLATIONAL MODIFICATIONS ON NUCLEOSOME STRUCTURE VIA ATOMIC FORCE MICROSCOPY

Four different histone proteins comprise the octameric histone core, a key component of DNA compaction into chromatin. The N-terminal tails of each histone protein contain a variety of post-translational modifications that can help modulate gene expression. Mutations are rare in these key proteins, but when found they are often linked to very serious and lethal diseases. In 2012, mutations in histone genes HIST1H2B and H3F3A were found to be implicated in brain cancer. The protein products of these genes produced four point mutants in two proteins: H3.1K27M, H3.3K27M, and H3.3G34R/V. The positions of these mutations are located at or adjacent to known sites of post-translational modification. Trimethylation of H3K27 is a known mark of gene repression and tumors harboring the K27M mutation have been found to have globally reduced levels of this mark. G34R/V mutations have been shown to produce locally reduced levels of H3K36 trimethylation as well. H3K36 trimethylation has been tied to transcription regulation and DNA repair. While these mutations are clearly disrupting the histone post-translational landscape they may also be perturbing the nucleosome structure itself. Histone proteins interact with DNA through basic residues. As these mutations are directly changing the basic residue content of the proteins, DNA-histone interactions may be altered. Research examining these mutations thus far have focused on secondary interactions between protein complexes and the nucleosome. No studies have examined how these mutations and changes in post-translational modifications could be effecting overall nucleosome structure. The main purpose of this research was to develop a system to examine the effects that these mutations have on nucleosome structure. To address this, Aim 1 of the project involved cloning eleven genes to produce the four canonical histone proteins (H2A, H2B, H3, and H4), an H3 variant (H3.3), three H3.3 point mutants (H3.3 K27M, H3.3 G34R, H3.3 G34V), one tailless H3.1 mutant (H3.1 Δ5), and two tailless H3.3 mutants (H3.3 Δ32 and H3.3 Δ45). A new, 2-step purification method was developed for the simple and inexpensive purification of histone proteins. Purified histones were then reconstituted into nucleosomes using a salt-gradient method. Four nucleosome constructs were reconstituted, differing only in the H3 protein included (H3.1, H3.3, H3.3 K27M, or H3.3 Δ32). Atomic Force Microscopy images of the four nucleosome constructs were acquired and analyzed. The location of these histone mutations is at or adjacent to known sites of post-translational modification. Due to this, it was necessary to determine how modifications at these sites effected the nucleosome structure as well to get a full scope of the impact of the mutations. Studying individual post-translational modifications is difficult as extracting histone proteins from tissue samples produces a heterogeneous population of modifications. To generate a homogenous population of modified proteins native chemical ligation methods were explored with the goal of producing histone proteins containing site-specific modifications. Preliminary ligation studies successfully created peptide dimers and trimers through a salicylaldehyde ester ligation technique

NECTAR: A New Algorithm for Characterizing and Correcting Noise in QToF-Mass Spectrometry Imaging Data

A typical mass spectrometry imaging experiment yields a very high number of detected peaks, many of which are noise and thus unwanted. To select only peaks of interest, data preprocessing tasks are applied to raw data. A statistical study to characterize three types of noise in MSI QToF data (random, chemical, and background noise) is presented through NECTAR, a new NoisE CorrecTion AlgoRithm. Random noise is confirmed to be dominant at lower m/z values (∼50–400 Da) while systematic chemical noise dominates at higher m/z values (>400 Da). A statistical approach is presented to demonstrate that chemical noise can be corrected to reduce its presence by a factor of ∼3. Reducing this effect helps to determine a more reliable baseline in the spectrum and therefore a more reliable noise level. Peaks are classified according to their spatial S/N on the single ion images, and background noise is thus removed from the list of peaks of interest. This new algorithm was applied to MALDI and DESI QToF data generated from the analysis of a mouse pancreatic tissue section to demonstrate its applicability and ability to filter out these types of noise in a relevant data set. PCA and t-SNE multivariate analysis reviews of the top 4000 peaks and the final 744 and 299 denoised peak list for MALDI and DESI, respectively, suggests an effective removal of uninformative peaks and proper selection of relevant peaks

Implications of peak selection in the interpretation of unsupervised mass spectrometry imaging data analyses

Mass spectrometry imaging can produce large amounts of complex spectral and spatial data. Such data sets are often analyzed with unsupervised machine learning approaches, which aim at reducing their complexity and facilitating their interpretation. However, choices made during data processing can impact the overall interpretation of these analyses. This work investigates the impact of the choices made at the peak selection step, which often occurs early in the data processing pipeline. The discussion is done in terms of visualization and interpretation of the results of two commonly used unsupervised approaches: -distributed stochastic neighbor embedding and -means clustering, which differ in nature and complexity. Criteria considered for peak selection include those based on hypotheses (exemplified herein in the analysis of metabolic alterations in genetically engineered mouse models of human colorectal cancer), particular molecular classes, and ion intensity. The results suggest that the choices made at the peak selection step have a significant impact in the visual interpretation of the results of either dimensionality reduction or clustering techniques and consequently in any downstream analysis that relies on these. Of particular significance, the results of this work show that while using the most abundant ions can result in interesting structure-related segmentation patterns that correlate well with histological features, using a smaller number of ions specifically selected based on prior knowledge about the biochemistry of the tissues under investigation can result in an easier-to-interpret, potentially more valuable, hypothesis-confirming result. Findings presented will help researchers understand and better utilize unsupervised machine learning approaches to mine high-dimensionality data

Exploring New Methods to Study and Moderate Proton Beam Damage for Multimodal Imaging on a Single Tissue Section

Characterizing proton beam damage in biological materials is of interest to enable the integration of proton microprobe elemental mapping techniques with other imaging modalities. It is also of relevance to obtain a deeper understanding of mechanical damage to lipids in tissues during proton beam cancer therapy. We have developed a novel strategy to characterize proton beam damage to lipids in biological tissues based on mass spectrometry imaging. This methodology is applied to characterize changes to lipids in tissues ex vivo, irradiated under different conditions designed to mitigate beam damage. This work shows that performing proton beam irradiation at ambient pressure, as well as including the application of an organic matrix prior to irradiation, can reduce damage to lipids in tissues. We also discovered that, irrespective of proton beam irradiation, placing a sample in a vacuum prior to desorption electrospray ionization imaging can enhance lipid signals, a conclusion that may be of future benefit to the mass spectrometry imaging community

Correlative Imaging of Trace Elements and Intact Molecular Species in a Single Tissue Sample at the 50 Micron Scale

Elemental and molecular imaging play a crucial role in understanding disease pathogenesis. To accurately correlate elemental and molecular markers, it is desirable to perform sequential elemental and molecular imaging on a single tissue section. However, very little is known about the impact of performing these measurements in sequence. In this work, we highlight some of the challenges and successes associated with performing elemental mapping in sequence with mass spectrometry imaging. Specifically, the feasibility of molecular mapping using the mass spectrometry imaging (MSI) techniques matrix assisted laser desorption ionisation (MALDI) and desorption electrospray ionisation (DESI) in sequence with the elemental mapping technique particle induced X-ray emission (PIXE) is explored. Challenges for integration include substrate compatibility, as well as delocalisation and spectral changes. We demonstrate that whilst sequential imaging comes with some compromises, sequential DESI-PIXE imaging is sufficient to correlate sulphur, iron and lipid markers in a single tissue section at the 50-micrometre scale

The amino acid transporter SLC7A5 is required for efficient growth of KRAS-mutant colorectal cancer

Oncogenic KRAS mutations and inactivation of the APC tumor suppressor co-occur in colorectal cancer (CRC). Despite efforts to target mutant KRAS directly, most therapeutic approaches focus on downstream pathways, albeit with limited efficacy. Moreover, mutant KRAS alters the basal metabolism of cancer cells, increasing glutamine utilization to support proliferation. We show that concomitant mutation of Apc and Kras in the mouse intestinal epithelium profoundly rewires metabolism, increasing glutamine consumption. Furthermore, SLC7A5, a glutamine antiporter, is critical for colorectal tumorigenesis in models of both early- and late-stage metastatic disease. Mechanistically, SLC7A5 maintains intracellular amino acid levels following KRAS activation through transcriptional and metabolic reprogramming. This supports the increased demand for bulk protein synthesis that underpins the enhanced proliferation of KRAS-mutant cells. Moreover, targeting protein synthesis, via inhibition of the mTORC1 regulator, together with Slc7a5 deletion abrogates the growth of established Kras-mutant tumors. Together, these data suggest SLC7A5 as an attractive target for therapy-resistant KRAS-mutant CRC