Maximizing Protein Extraction: Simplified Homogenization Workflow using Dry Pulverization and AFA Ultrasonication Across Sample Types

At ASMS 2023, Dr. Meagan Gadzuk-Shea and Dr. Juan Wang, Senior Scientists at AstraZeneca, shared their strategies for streamlining the homogenization process, optimizing protein extraction, and reducing sample variance in mass spec analysis using Covaris’ Adaptive Focused Acoustics® technology and dry pulverization. Read our summary below to learn more.

Introduction

Traditional tissue processing workflows that utilize bead mills are confronted with a multitude of challenges. From variability and inefficient protein extraction, to heat generation that can damage proteins and impact reliability of downstream data, there’s much room for improvement. Moreover, this method is not only laborious and time-consuming, but also constrained by the availability of consumables.

To address these issues, AstraZeneca partnered with Covaris to incorporate their cryoPREP® dry pulverizer and Adaptive Focused Acoustics (AFA) technology into its workflow. What they found is that Covaris AFA and cryoPREP proved to be an excellent solution in terms of the quantity of protein extracted, the data quality, and the reduction in hands on time by the lab. In this blog, we’ll explore how the team optimized the workflow parameters to maximize extraction, reduce variation, improve data quality, and minimize active sample handling.

AFA and Dry Pulverization Streamlines Workflow

AstraZeneca introduced two Covaris technologies into their workflow: cryoPREP automated dry pulverization and AFA, with the hope of improving the efficiency and effectiveness of their process. For dry pulverization, the team used Covaris’ CP02 cryoPREP Extraction System, which utilizes a calibrated and controlled mechanical force to turn flash frozen tissues into a fine powder. This disrupts the extracellular matrix, increasing surface area and reducing the distance that the lysis buffer has to cover to extract those proteins from the sample. AFA is then used for homogenization and lysis using Covaris’ LE220Rsc, a system that is known for its speed and easy integration into workflows. It localizes ultrasonic acoustic energy onto samples, ensuring efficient and effective protein extraction.

This workflow is a multi-step process that includes weighing tissues, flash freezing them in a Covaris tissueTUBE, and then cryopulverizing them twice. The powdered sample is moved into a Covaris milliTUBE with an AFA fiber, and then homogenization and lysis are performed using a Covaris Focused-ultrasonicator. BCA analysis is performed for protein qualification before proceeding to global proteomics, with protein extraction and coefficient of variation (CV) in protein identifications used as evaluation metrics for optimization work (Figure 1).

Figure 1. Experimental design and workflow.

Case Study Demonstrates More Effective Protein Extraction, Improved Data Quality and Less Labor Intensity

AstraZeneca explored several key parameters:

  • Peak Incident Power (PIP): The amplitude of acoustic waves impacting the sample.
  • Duty factor: How long the sample is actively treated with acoustics during one cycle.
  • Cycles per burst: The number of acoustic waves that the samples are hit with at this PIP during each treatment burst, with length set by treatment frequency.

The first step was to establish a baseline for the workflow, starting with 350-watt PIP and a slightly higher duty factor, setting the cycles per burst to 1000. The reference point was AstraZeneca’s manual bead homogenization process, which consisted of adding tissue to a homogenizing ceramic bead consumable, followed by 3-5 cycles of bead beading, cooling, heat denaturation and spinning down samples, before proceeding with downstream preparation. It was assumed that every 10 mg of tissue would yield 1 mg of protein (Figure 2).

Figure 2. Establishing baseline for the Covaris workflow. It was assumed that every 10 mg of tissue would yield 1 mg of protein.

The bead mill method achieved less than the desired 20% covariance in protein groups identified by mass spec analysis, while Covaris AFA exceeded this benchmark. To optimize protein extraction and improve data quality, the team modified certain AFA process parameters. Initial tests were conducted on liver tissue, comparing AFA treatments with different settings for parameters such as PIP and dithering (Figures 3 and 4).

Figure 3. Testing parameters for liver tissue. PIP, dithering, and heat cycle AFA parameters were adjusted in an effort to maximize protein extraction.

Figure 4. Protein extraction using different AFA parameters for liver tissue. All the AFA methods resulted in at least twice the extracted protein compared to the manual method.

AstraZeneca ran three AFA test methods with different parameters to identify the best workflow for their needs. Figures 4 and 5 demonstrate the results of the three tested methods (AFA 1, AFA 2 and AFA 3) and their subsequent protein extractions. Results from the manual approach indicated that only ~50% of the expected yield was achieved. However, all three AFA treatment methods resulted in maximum protein extraction. Despite slightly exceeding the covariance limit, this data suggests that they were still successful in extracting more protein. In fact, at least twice the amount of extracted protein was obtained compared to the manual bead mill treatments, with a lower covariance in those extracted amounts, specifically for AFA 1.

Figure 5. Results of three tested methods (AFA 1, AFA 2 and AFA 3) and their subsequent protein extractions.

Upon analyzing the mass spec data, AFA 1 exhibited excellent covariance at ≤10%, prompting further testing on various tissue types such as liver, brain, kidney, spleen, and heart. Results from subsequent testing showed the settings for AFA 1 worked well for spleen tissue, while harder tissue types required adjustments to the parameters.

This is where the concept of dithering came into play. The team tested six AFA methods, introducing a dual dithering treatment in AFA 5 and AFA 6. This involved shifting treatment direction after four minutes, mixing the tissue thoroughly to enhance protein extraction. AFA 5 and AFA 6 showed maximum protein extraction and excellent CVs from these methods, unlike AFA 4, which didn’t have dual treatment (Figure 6).

Figure 6.The team tested six AFA methods, introducing a dual dithering treatment in AFA 5 and AFA 6.

Next, the team used AFA 5 and AFA 6 on tissues that had previously posed challenges, noting excellent extraction and tight distributions. All treatments tested fell within the desired threshold of covariance at the mass spec level. As a result, they moved forward with AFA 5 to perform further tests on different tissues (Figure 7).

Figure 7. AFA 5 was further tested on kidney and brain tissues. Results shown.

It’s important to note that this preparation had some setbacks – including sample delays in the queue, which led to evaporation and increased variability. Despite these challenges, excellent levels of protein extraction were observed, which seemed unaffected by the evaporation problem. Mass spec data also showed CV values near the desired 20% benchmark (Figure 8).

Figure 8. Excellent levels of protein extraction were found; MS data also showed CV values near desired 20% benchmark.

Covaris Technology Maximizes Protein Extraction with Less Than Half the Hands-on Time

AstraZeneca also aimed to reduce the amount of hands-on time and streamline workflows. As Figure 9 illustrates, the traditional bead mill process requires more hands-on steps than AFA. Roughly 70% of time is spent working with samples in the bead mill process. However, with Covaris technology, it’s reduced to 33%, enabling maximum protein extraction with less involvement.

Figure 9. A comparison of hands-on time: 24 sample prep.

Conclusion

AstraZeneca has made significant strides toward optimizing their parameters. While there’s always room for improvement, Covaris’ AFA method has proven to be more effective, consistently extracting twice the amount of protein and improving data quality while reducing hands-on time.

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