FAQ

What is the Corpuscle problem?

The Corpuscle Problem is the bias (systematic error) introduced by attempting to quantify the number or density of 3-D objects (cells) based on the number of 2-D profiles on a standard histological section. This bias occurs because cells with a larger size, more complex shape, and/or their long axis perpendicular to the sectioning direction are more likely to appear in the cell count, which leads to a biased count. The disector principle introduced by Sterio (1984) avoids this problem by counting all 3-D objects regardless of their size, shape and orientation.

A more or less clustered distribution does not affect accuracy but does require adjusting your sampling strategy to achieve maximal efficiency. In a pilot study, Stereologer automatically identifies the major sources of variability in your data and adjusts your sampling parameters accordingly. In this way the system optimizes your sampling effort for maximum efficiency, which dramatically increases the throughput of your studies.

The cost of a Stereologer system includes complimentary, no-cost maintenance and support. Once you set it up in your lab, the SRC helps you get started. The software is extremely user-friendly (not all computerized stereology systems are), which is important for someone who has never used computerized systems before. If there are any software issues, the stereology support team will provide a response as soon as we can.

Dear SRC Biosciences: I am using the Stereologer to analyze stained tissue sections through the brain of a mouse model of Alzheimer’s disease.  Specifically, I’m using a single disector to count amyloid plaques which are relatively rare; and microglia cells which are far more plentiful.

As you can see from the attached images and results, this approach is working well for my plaque counts; however, I feel like I’m counting counting far too many cells (>700), which is taking far more time than necessary for analyzing each case.

My question is: How can I optimize my date collection for counting both cells and plaques using a single pass through the stained tissue sections for each mouse brain?

SRC Biosciences: Thank you for providing the information and question about your analysis of amyloid plaques and cell number in mouse brain sections.

You are right.  Using a single disector with the same Frame Area for both objects (plaques, cells) forces you to count far too many cells than necessary.  I suggest the following 2-step process to optimize the efficiency of your data collection.

1. Start a New Study.  On the first page (Study Initialization) add Number (Parameter) and Disector (Probe) twice.  Name one Number-Disector combination “Plaques” and the second Number-Disector combination “Cells.”  This will allow you to use disectors with optimal Frame Areas to separately count plaques and cells at each X-Y location.
2. Next you need to set different Frame Areas for these disector frames.  On the Probe Initialization page, click Edit for High Magnification then set the Frame Area for Plaques to 50% of the Window Area.  Second, use the same approach to Edit the Frame Area for Cells using a smaller area, perhaps 10% of the Window.

Otherwise, use all the same Settings and Sampling Parameters for counting for both types of objects.

Lastly, I suggest you do a Pilot Study using this study design to confirm that you count on average ~ 0 to 5 Plaques or Cells in each Disector.  This will accomplish you desired goal of optimizing the time for counts of plaques and cells using a single pass through the sections for each case. 

Feel free to send me your results, if you like, for a quick review.  Also, let me know if you would like to arrange a Remote Support session to answer any further questions.