Image analysis in proteomics MCQs With Answer

Image analysis in proteomics is an essential skill for M.Pharm students focusing on proteins and formulations. This blog-style quiz set introduces practical and theoretical aspects of extracting quantitative data from proteomic images such as 1D/2D gels, western blots, fluorescence microscopy and mass spectrometry imaging. Questions emphasize image preprocessing, spot detection, normalization, statistical validation, and commonly used software tools and algorithms. The aim is to deepen understanding of how imaging choices (staining, bit depth, resolution) and analysis methods (background subtraction, thresholding, registration) influence accuracy and reproducibility of proteomic quantification. These MCQs are tailored to test and expand analytical thinking for lab and research applications.

Q1. What is the primary goal of image analysis in proteomics?

• To visually enhance proteomic images for publication
• To extract quantitative information such as spot intensity, size and position from proteomic images
• To replace mass spectrometry for protein identification
• To convert protein sequences into image representations

Correct Answer: To extract quantitative information such as spot intensity, size and position from proteomic images

Q2. Which sequence of steps best describes typical 2D-PAGE image analysis?

• Protein digestion → MALDI analysis → database search → spot detection
• Image acquisition → background subtraction → spot detection and quantification → spot matching across gels
• Staining → mass spectrometry → image registration → normalization
• Segmentation → de novo sequencing → intensity normalization → spot picking

Correct Answer: Image acquisition → background subtraction → spot detection and quantification → spot matching across gels

Q3. Which software is widely used and extensible for general-purpose proteomic image analysis with many plugins available?

• Microsoft Excel
• ImageJ/Fiji
• PyMOL
• BLAST

Correct Answer: ImageJ/Fiji

Q4. Which algorithm name is commonly used for background subtraction in gel and blot images?

• Fourier transform filtering
• Rolling ball algorithm
• Watershed segmentation
• Otsu thresholding

Correct Answer: Rolling ball algorithm

Q5. Which normalization method is frequently applied to correct for overall intensity differences between gel images?

• Normalization by image width
• Total intensity (sum) normalization across detected spots
• Normalization by bit depth only
• Normalization using file creation date

Correct Answer: Total intensity (sum) normalization across detected spots

Q6. How does image bit depth influence proteomic image analysis?

• It controls the pixel physical dimensions
• It determines maximum signal levels and dynamic range of the recorded image
• It replaces the need for normalization
• It defines the stain affinity of proteins

Correct Answer: It determines maximum signal levels and dynamic range of the recorded image

Q7. What metric is commonly used when quantifying spot amount by densitometry?

• Integrated optical density (IOD)
• Pixel circumference index
• Spatial frequency count
• Histogram mean color

Correct Answer: Integrated optical density (IOD)

Q8. What approach is typically used to align multiple 2D gel images before spot matching?

• Random translation of spot coordinates
• Landmark-based warping and elastic alignment
• Global color inversion
• Histogram equalization only

Correct Answer: Landmark-based warping and elastic alignment

Q9. Compared to Coomassie staining, silver staining for gels is characterized by which trade-off?

• Silver is less sensitive and less expensive than Coomassie
• Silver is more sensitive but has a narrower linear dynamic range than Coomassie
• Silver provides sequence information directly
• Silver eliminates the need for image normalization

Correct Answer: Silver is more sensitive but has a narrower linear dynamic range than Coomassie

Q10. In statistical analysis of spot intensity differences across conditions, what does controlling the false discovery rate (FDR) do?

• Minimizes the number of true positives
• Controls the expected proportion of false positives among declared significant results
• Ensures all null hypotheses are retained
• Guarantees zero type I errors

Correct Answer: Controls the expected proportion of false positives among declared significant results

Q11. What is the aim of deconvolution in fluorescence proteomic imaging?

• To blur the image for aesthetic purposes
• To reverse the microscope point-spread function and improve spatial resolution
• To convert intensity units to concentration directly
• To change bit depth after acquisition

Correct Answer: To reverse the microscope point-spread function and improve spatial resolution

Q12. Which automatic global thresholding method is commonly used to segment spots from background?

• K-means clustering
• Otsu’s method
• Principal component thresholding
• Fourier-based thresholding

Correct Answer: Otsu’s method

Q13. For mass spectrometry imaging (MSI) data, which normalization is standard to compensate for varying total signal between spectra?

• Normalization by pixel coordinates
• Total ion current (TIC) normalization
• Normalization by acquisition date
• Normalization to the brightest pixel only

Correct Answer: Total ion current (TIC) normalization

Q14. Which multivariate method is commonly used to reduce dimensionality of high-content proteomic image datasets prior to pattern discovery?

• Benjamini-Hochberg correction
• Principal component analysis (PCA)
• Rolling ball subtraction
• Otsu thresholding

Correct Answer: Principal component analysis (PCA)

Q15. Which practical strategy improves signal-to-noise ratio (SNR) in proteomic imaging?

• Decrease exposure time and avoid filtering
• Denoising (e.g., median or Gaussian filters) and increasing appropriate acquisition signal
• Always use 8-bit images to lower variability
• Convert images to monochrome after segmentation only

Correct Answer: Denoising (e.g., median or Gaussian filters) and increasing appropriate acquisition signal

Q16. When configuring automated spot detection, which parameters are most critical to set?

• Minimum area, intensity threshold and shape criteria (e.g., circularity)
• File name length and image creation time
• Bit depth only, without intensity thresholds
• Chromatic aberration coefficient

Correct Answer: Minimum area, intensity threshold and shape criteria (e.g., circularity)

Q17. Which metric is widely used to quantify co-localization between two fluorescently labeled proteins in an image?

• Root-mean-square deviation
• Pearson correlation coefficient
• Integrated optical density difference
• Otsu overlap index

Correct Answer: Pearson correlation coefficient

Q18. For absolute quantification of a protein spot using imaging, what is the best practice?

• Use isotopically labeled internal standards spiked at known amounts
• Rely solely on relative intensity compared to an arbitrary lane
• Use file bit depth as concentration proxy
• Compare to the mean pixel value of the whole image

Correct Answer: Use isotopically labeled internal standards spiked at known amounts

Q19. Which registration type is most appropriate to correct non-uniform distortions between two gel images?

• Rigid registration (translation and rotation only)
• Nonrigid (elastic) registration that allows local deformations
• Color histogram matching
• Global contrast stretching

Correct Answer: Nonrigid (elastic) registration that allows local deformations

Q20. When testing intensities of many spots for differential expression, which multiple testing correction is commonly applied to control FDR?

• Bonferroni correction only
• Benjamini-Hochberg procedure
• No correction is necessary for image data
• Median normalization followed by no statistical test

Correct Answer: Benjamini-Hochberg procedure

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