Processing MALDI Mass Spectra to Improve Mass Spectral Direct Tissue Analysis

Abstract

Profiling and imaging biological specimens using MALDI mass spectrometry has significant potential to contribute to our understanding and diagnosis of disease. The technique is efficient and high-throughput providing a wealth of data about the biological state of the sample from a very simple and direct experiment. However, in order for these techniques to be put to use for clinical purposes, the approaches used to process and analyze the data must improve. This study examines some of the existing tools to baseline subtract, normalize, align, and remove spectral noise for MALDI data, comparing the advantages of each. A preferred workflow is presented that can be easily implemented for data in ASCII format. The advantages of using such an approach are discussed for both molecular profiling and imaging mass spectrometry.

Fig. 1

Mass spectra analysis work flow. The mass spectra are treated to processing algorithms responsible for the removal of noise, realignment of the m/z scale, and peak selection and matching. The data is returned to a table, formatted for statistical analysis using a number of established methods. The result of the analysis is a list of biomarker candidates that are subjected to further validation steps.

Fig. 2

MALDI MS profile spectra acquired from three sections from different tissues. (a) Raw spectra; (b) spectra that have been baseline corrected using ProTSData software.

Fig. 3

The effect of normalization on data quality. (a) Single spectra compared against the group average deviate significantly from linearity, whereas (b) the same comparison made after normalization (TIC) results in a linear relationship. (c) This comparison was repeated for each spectrum individually, and the correlation coefficients and slope reported in histogram form. Total ion current (TIC) normalization is the most effective at reducing the deviation from linearity and increasing the correlation coefficient. (Linest and R2∼1.) Linest = regression coefficient of the best fit line (best fit = 1.0). R² = square of the Pearson product moment correlation coefficient. (proportion of variance in y attributed to variance in x; least variance = 1.0)

Fig. 4

Baseline correction and peak binning. A portion of the data before (a), and after (b) spectral alignment. Spectral alignment is performed using common peaks across the set of spectra. Peaks are selected that are common to 90% of the data collected. These features are used as arbitrary calibrants for a quadratic calibration of the data. (c) The selected peaks from the data set are categorized into bins, grouping peaks originating from the same ion.

Fig. 5

(a) Comparison of the relationship between protein concentration and instrument response for processed and unprocessed data. The unprocessed data shows a marked deviation from the expected relationship to protein concentration. However, processing the data according to the proposed scheme restores data quality comparable to the ideal case. The factors that contribute to this deviation include manual sample preparation, MALDI target spotting, and sample acquisition. (b) Effect of baseline/noise subtraction to remove chemical noise and the effect of normalization on spectral quality. Spectra were normalized using TIC. Spectral features were extracted using a minimum S/N threshold of 3. Each spectrum was comprised of 5-7 ions originating from the spiked standard proteins and approximately 110-120 ions originating from the liver extract. The error bars represent 95% confidence intervals for the intensity.

Fig. 6

Baseline subtraction and normalization improve image quality. Two selected ion images obtained from mouse brain are chosen to highlight the advantages of spectral processing on image quality. Above are compared raw image data (a and c) and data processed (b and d) to remove baseline and normalized using total ion current. The inset in panel (c) presents a photomicrograph of the section used for IMS after matrix removal and Nissl staining. Red arrow, cortex; green arrow, hippocampus.