Allison L. Dill

Latent Fingerprint Chemical Imaging by Mass Spectrometry

Latent fingerprints (LFPs) potentially contain more forensic information than the simple identification of the subject; they may contain evidence of contacts with explosives or substances of abuse. Chemical information can also be useful in resolving overlapping LFPs from different individuals. We used desorption electrospray ionization mass spectrometry in an imaging mode to record compound-specific chemical fingerprints.

Mass Spectrometry Imaging under Ambient Conditions

Mass spectrometry imaging (MSI) has emerged as an important tool in the last decade and it is beginning to show potential to provide new information in many fields owing to its unique ability to acquire molecularly specific images and to provide multiplexed information, without the need for labeling or staining. In MSI, the chemical identity of molecules present on a surface is investigated as a function of spatial distribution. In addition to now standard methods involving MSI in vacuum, recently developed ambient ionization techniques allow MSI to be performed under atmospheric pressure on untreated samples outside the mass spectrometer. Here we review recent developments and applications of MSI emphasizing the ambient ionization techniques of desorption electrospray ionization (DESI), laser ablation electrospray ionization (LAESI), probe electrospray ionization (PESI), desorption atmospheric pressure photoionization (DAPPI), femtosecond laser desorption ionization (fs-LDI), laser electrospray mass spectrometry (LEMS), infrared laser ablation metastable-induced chemical ionization (IR-LAMICI), liquid microjunction surface sampling probe mass spectrometry (LMJ-SSP MS), nanospray desorption electrospray ionization (nano-DESI), and plasma sources such as the low temperature plasma (LTP) probe and laser ablation coupled to flowing atmospheric-pressure afterglow (LA-FAPA). Included are discussions of some of the features of ambient MSI for example the ability to implement chemical reactions with the goal of providing high abundance ions characteristic of specific compounds of interest and the use of tandem mass spectrometry to either map the distribution of targeted molecules with high specificity or to provide additional MS information on the structural identification of compounds. We also describe the role of bioinformatics in acquiring and interpreting the chemical and spatial information obtained through MSI, especially in biological applications for tissue diagnostic purposes. Finally, we discuss the challenges in ambient MSI and include perspectives on the future of the field.

Classifying Human Brain Tumors by Lipid Imaging with Mass Spectrometry

Brain tissue biopsies are required to histologically diagnose brain tumors, but current approaches are limited by tissue characterization at the time of surgery. Emerging technologies such as mass spectrometry imaging can enable a rapid direct analysis of cancerous tissue based on molecular composition. Here, we illustrate how gliomas can be rapidly classified by desorption electrospray ionization-mass spectrometry (DESI-MS) imaging, multivariate statistical analysis, and machine learning. DESI-MS imaging was carried out on 36 human glioma samples, including oligodendroglioma, astrocytoma, and oligoastrocytoma, all of different histologic grades and varied tumor cell concentration. Gray and white matter from glial tumors were readily discriminated and detailed diagnostic information could be provided. Classifiers for subtype, grade, and concentration features generated with lipidomic data showed high recognition capability with more than 97% cross-validation. Specimen classification in an independent validation set agreed with expert histopathology diagnosis for 79% of tested features. Together, our findings offer proof of concept that intraoperative examination and classification of brain tissue by mass spectrometry can provide surgeons, pathologists, and oncologists with critical and previously unavailable information to rapidly guide surgical resections that can improve management of patients with malignant brain tumors.

Cholesterol Sulfate Imaging in Human Prostate Cancer Tissue by Desorption Electrospray Ionization Mass Spectrometry

Development of methods for rapid distinction between cancerous and non-neoplastic tissues is an important goal in disease diagnosis. To this end, desorption electrospray ionization mass spectrometry (DESI-MS) imaging was applied to analyze the lipid profiles of thin tissue sections of 68 samples of human prostate cancer and normal tissue. The disease state of the tissue sections was determined by independent histopathological examination. Cholesterol sulfate was identified as a differentiating compound, found almost exclusively in cancerous tissues including tissue containing precancerous lesions. The presence of cholesterol sulfate in prostate tissues might serve as a tool for prostate cancer diagnosis although confirmation through larger and more diverse cohorts and correlations with clinical outcome data is needed.

Desorption Electrospray Ionization Mass Spectrometry for Lipid Characterization and Biological Tissue Imaging

Desorption electrospray ionization mass spectrometry (DESI-MS) imaging of biological samples allows untargeted analysis and structural characterization of lipids ionized from the near-surface region of a sample under ambient conditions. DESI is a powerful and sensitive MS ionization method for 2D and 3D imaging of lipids from direct and unmodified complex biological samples. This review describes the strengths and limitations of DESI-MS for lipid characterization and imaging together with the technical workflow and a survey of applications. Included are discussions of lipid mapping and biomarker discovery as well as a perspective on the future of DESI imaging.

Mass spectrometric imaging of lipids using desorption electrospray ionization

Desorption electrospray ionization (DESI), a relatively new ambient ionization technique used in mass spectrometry (MS), allows for the direct analysis of samples such as thin tissue sections, to be conducted outside of vacuum in the ambient environment and often without sample preparation. DESI-MS has been used in order to systematically characterize phospholipids, which are abundant species in biological tissue samples. Lipids play important biological roles and differences in lipid compositions have been seen in diseases such as cancer and Alzheimers disease. Imaging of thin tissue sections exploits the ability of DESI-MS to study these lipids directly in the biological matrix. In imaging MS (IMS), a mass spectrum is recorded at each pixel while moving the surface containing the sample so that the entire sample area is covered. The information in these mass spectra can be combined to create a 2D chemical image of the sample, combining information on spatial distribution with information on chemical identity from the characteristic ions in the mass spectra. DESI-MS has been used to image a variety of tissue samples including human liver adenocarcinoma, rat brain, human breast tissue and canine abdominal tumor tissue. Comparisons between diseased and normal tissue are made in these studies.

Multivariate statistical differentiation of renal cell carcinomas based on lipidomic analysis by ambient ionization imaging mass spectrometry.

AbstractDesorption electrospray ionization (DESI) mass spectrometry (MS) was used in an imaging mode to interrogate the lipid profiles of thin tissue sections of 11 sample pairs of human papillary renal cell carcinoma (RCC) and adjacent normal tissue and nine sample pairs of clear cell RCC and adjacent normal tissue. DESI-MS images showing the spatial distributions of particular glycerophospholipids (GPs) and free fatty acids in the negative ion mode were compared to serial tissue sections stained with hematoxylin and eosin (H&E). Increased absolute intensities as well as changes in relative abundance were seen for particular compounds in the tumor regions of the samples. Multivariate statistical analysis using orthogonal projection to latent structures treated partial least square discriminate analysis (PLS-DA) was used for visualization and classification of the tissue pairs using the full mass spectra as predictors. PLS-DA successfully distinguished tumor from normal tissue for both papillary and clear cell RCC with misclassification rates obtained from the validation set of 14.3% and 7.8%, respectively. It was also used to distinguish papillary and clear cell RCC from each other and from the combined normal tissues with a reasonable misclassification rate of 23%, as determined from the validation set. Overall DESI-MS imaging combined with multivariate statistical analysis shows promise as a molecular pathology technique for diagnosing cancerous and normal tissue on the basis of GP profiles. FigureMolecular disease diagnostics by DESI without sample preparation. a Good information is obtained by mapping the distribution of individual compounds in the tissue (e.g., PI(18:0/20:4). b Even better discrimination between tumor and healthy tissue is achieved using PLS-DA to consider all the data after having established through a training set of samples the features that correlate with disease as recognized by standard H&E stain pathological examination

Lipid profiles of canine invasive transitional cell carcinoma of the urinary bladder and adjacent normal tissue by desorption electrospray ionization imaging mass spectrometry.

Desorption electrospray ionization mass spectrometry (DESI-MS) was used in an imaging mode to interrogate the lipid profiles of thin tissue sections of canine spontaneous invasive transitional cell carcinoma of the urinary bladder (a model of human invasive bladder cancer) as well as adjacent normal tissue from four different dogs. The glycerophospholipids and sphingolipids that appear as intense signals in both the negative ion and positive ion modes were identified by tandem mass spectrometry product ion scans using collision-induced dissociation. Differences in the relative distributions of the lipid species were present between the tumor and adjacent normal tissue in both the negative and positive ion modes. DESI-MS images showing the spatial distributions of particular glycerophospholipids, sphinoglipids, and free fatty acids in both the negative and positive ion modes were compared to serial tissue sections that were stained with hematoxylin and eosin (H&E). Increased absolute and relative intensities for at least five different glycerophospholipids and three free fatty acids in the negative ion mode and at least four different lipid species in the positive ion mode were seen in the tumor region of the samples in all four dogs. In addition, one sphingolipid species exhibited increased signal intensity in the positive ion mode in normal tissue relative to the diseased tissue. Principal component analysis was also used to generate unsupervised statistical images from the negative ion mode data, and these images are in excellent agreement with the DESI images obtained from the selected ions and also the H&E-stained tissue.

New ionization methods and miniature mass spectrometers for biomedicine: DESI imaging for cancer diagnostics and paper spray ionization for therapeutic drug monitoring

The state-of-the-art in two new ambient ionization methods for mass spectrometry, desorption electrospray ionization (DESI) and paper spray (PS), is described and their utility is illustrated with new studies on tissue imaging and biofluid analysis. DESI is an ambient ionization method that can be performed on untreated histological sections of biological tissue in the open lab environment to image lipids, fatty acids, hormones and other compounds. Paper spray is performed in the open lab too; it involves electrospraying dry blood spots or biofluid deposits from a porous medium. PS is characterized by extreme simplicity and speed: a spot of whole blood or other biofluid is analyzed directly from paper, simply by applying a high voltage to the moist paper. Both methods are being developed for use in diagnostics as a means to inform therapy. DESI imaging is applied to create molecular maps of tissue sections without prior labeling or other sample preparation. Like other methods of mass spectrometry imaging (MSI), it combines the chemical speciation of multiple analytes with information on spatial distributions. DESI imaging provides valuable information which correlates with the disease state of tissue as determined by standard histochemical methods. Positive-ion data are presented which complement previously reported negative-ion data on paired human bladder cancerous and adjacent normal tissue sections from 20 patients. These data add to the evidence already in the literature demonstrating that differences in the distributions of particular lipids contain disease-diagnostic information. Multivariate statistical analysis using principal component analysis (PCA) is used to analyze the imaging MS data, and so confirm differences between the lipid profiles of diseased and healthy tissue types. As more such data is acquired, DESI imaging has the potential to be a diagnostic tool for future cancer detection in situ; this suggests a potential role in guiding therapy in parallel with standard histochemical and immunohistological methods. The PS methodology is aimed at high-throughput clinical measurement of quantitative levels of particular therapeutic agents in blood and other biofluids. The experiment allows individual drugs to be quantified at therapeutic levels and data is presented showing quantitative drug analysis from mixtures of therapeutic drugs in whole blood. Data on cholesterol sulfate, a new possible prostate biomarker seen at elevated levels in diseased prostate tissue, but not in healthy prostate tissue in serum are reported using paper spray ionization.

Multivariate Statistical Identification of Human Bladder Carcinomas Using Ambient Ionization Imaging Mass Spectrometry

Diagnosis of human bladder cancer in untreated tissue sections is achieved by using imaging data from desorption electrospray ionization mass spectrometry (DESI-MS) combined with multivariate statistical analysis. We use the distinctive DESI-MS glycerophospholipid (GP) mass spectral profiles to visually characterize and formally classify twenty pairs (40 tissue samples) of human cancerous and adjacent normal bladder tissue samples. The individual ion images derived from the acquired profiles correlate with standard histological hematoxylin and eosin (H&E)-stained serial sections. The profiles allow us to classify the disease status of the tissue samples with high accuracy as judged by reference histological data. To achieve this, the data from the twenty pairs were divided into a training set and a validation set. Spectra from the tumor and normal regions of each of the tissue sections in the training set were used for orthogonal projection to latent structures (O-PLS) treated partial least-square discriminate analysis (PLS-DA). This predictive model was then validated by using the validation set and showed a 5% error rate for classification and a misclassification rate of 12%. It was also used to create synthetic images of the tissue sections showing pixel-by-pixel disease classification of the tissue and these data agreed well with the independent classification that uses histological data by a certified pathologist. This represents the first application of multivariate statistical methods for classification by ambient ionization although these methods have been applied previously to other MS imaging methods. The results are encouraging in terms of the development of a method that could be utilized in a clinical setting through visualization and diagnosis of intact tissue.