The need to improve microscopy trainings

The LCI facility has just published an article about  ‘Improving light microscopy training routines with evidence-based education’. The article is now available in the Journal of Microscopy. We hope that this article will give useful tips to better design users’ training and improve their learning outcome. 🙂

Here is the abstract:

The low reproducibility of scientific data published in articles has recently become a cause of concern in many scientific fields. Data involving light microscopy is no exception. The low awareness of researchers of the technologies they use in their research has been identified as one of the main causes of the problem. Potential solutions have hinted at the need to improve technological and methodological education within research.

Despite the pivotal role of microscopy core facilities in the education of researchers being well documented, facility staff (FS) often learn their trade on the job, without receiving themselves any structured education about the technology they teach others to use. Additionally, despite endorsing an important role at the highest level of education, most FS never receive any training in pedagogy, the field of research on teaching and learning methods.

In this article, we argue that the low level of awareness that researchers have of microscopy stems from a knowledge gap formed between them and microscopy FS during training routines. On the one hand, FS consider that their teaching task is to explain what is needed to produce reliable data. On the other, despite understanding what is being taught, researchers fail to learn the most challenging aspects of microscopy, those involving their judgement and reasoning. We suggest that the misunderstanding between FS and researchers is due to FS not being educated in pedagogy and thus often confusing understanding and learning.

To bridge this knowledge gap and improve the quality of the microscopy education available to researchers, we propose a paradigm shift where training staff at technological core facilities be acknowledged as full-fledged teachers and offered structured education not only in the technology they teach but also in pedagogy. We then suggest that training routines at facilities be upgraded to follow the principles of the Constructive Alignment pedagogical method. We give an example of how this can be applied to existing microscopy training routines. We also describe a model to define where the responsibility of FS in training researchers begins and ends.

This involves a major structural change where university staff involved in teaching research technologies themselves receive appropriate education. For this to be achieved, we advocate that funding agencies, universities, microscopy and core facility organisations mobilise resources of time and funding. Such changes may involve funding the creation and development of ‘Train-the-trainer’ type of courses and giving incentives for FS to upgrade their technological and pedagogical knowledge, for example by including them in career paths. We believe that this paradigm shift is necessary to improve the level of microscopy education and ultimately the reproducibility of published data.

Cool stuff one can do with the LCI Primo

At the LCI core facility, you can use a machine called Primo to micropattern/print proteins at the bottom of a microscopy dish and enable real time imaging of cell-protein interactions.

Now we learnt that we can even micropattern lipids and antibodies on microscopy dishes to image how cells interact with these molecules! This can be done with any pattern and even in multiwell plates! 🙂

Lipid micropatterning: Here is a cool paper showing how filopodia interact with sourrounding lipids and proteins. They image with Structured Illumination and TIRF, techniques that are also on offer at our facility. 🙂

Antibody micropatterning: A new way to analyse extracellular vesicles with multiplexed detection of proteins and RNAs at single EV resolution.

The power of Deep Neural networks

I am wowed at the images published recently in BioArxiv in the Zero-shot deconvolution networks paper. It will be interesting to see the peer-reviewed paper.

I would be interested in visualizing the amount of errors made by the network. A simple way to do it is to acquire a short time lapse (eg 10 images) of a fixed sample, run it through the network and see which structures are stably identified and which change from frame to frame. 🙂

 

Light-seq: Multiplexed, non-destructive spatial transcriptomics of tissues sections using light

Light-Seq is a new pretty cool technique for highly multiplexed sequencing of RNA in tissue sections using light. This technique is highly sensitive, highly spatially resolved and because it does not destroy the tissue, it can be combined with protein labelling (genetic or by immunolabelling).

On one of our single-point confocal/spinning disk/widefield system at the LCI facility, we have a device called Primo (DMD + UV laser) which can be used to run this technique! 🙂

Let us know if you would like to set up LightSeq at the LCI core facility!

mCherry-XL: brigther, more stable and with better spectra!

mCherry is a very popular red fluorescent protein. However it has several disadvantages:

  • It is shifted towards far red (ex peak 585 nm) so it often is not imaged optimally with the illumination sources and filters commonly available.
  • It bleaches fast

mCherry has now been evolved into mCherry-XL with several improvements:

  • This variant is shifted back towards green (ex peak 560 nm) therefore being very well excited with popular 561nm lasers.
  • It is 3 times brighter than mCherry
  • There is also a clear improvement in the lifetime for FLIM
  • Together the 2 points above means that less excitation power is required so it should help with the bleaching problem

Here is the paper. Therefore you should consider mCherry-XL for your future tagging with red fluorescence proteins.

CLIJ2: Open Computing Language and ImageJ2, GPU power for everyone

CLIJ2 allows you to use ImageJ/Fiji on GPU instead if CPU processing, so much faster! 🙂

Here is a nice article about what CLIJ2 can do. By the way, this article is published on a new imaging forum called FocalPlane. Check it out! And here is the presentation of how to use CLIJ2 at one of the recent Neubias event in May 2020.

If you have an analysis pipeline built in Fiji, Icy or Matlab and processing takes a long time, CLIJ2 will help you a lot.

One-step non-toxic clearing protocol

Do you know that clearing is not just about light sheet microscopy? Even if you have done your job well and your sample is directly on the coverslip (not on the slide), as soon as your sample is thicker than 10 um (1 cell diameter), you will see the effect of the refraction index mismatch.

What is that? Your sample and the mounting medium around it have a certain refraction index (or likely several). The objective you are using is designed for a certain refraction index (e.g. air, water or oil). If these refraction indices do not match what happens? as soon as you image a tiny bit away from the coverslip, the sample will look elongated, the intensity and contrast will drop very fast.

Sounds familiar? If yes, changing your mounting medium to match the objective will solve the problem. It works for light sheet but it also works for wide field or confocal imaging! Just change your mounting medium and you will see an enormous difference!

Here is an article describing a one-step clearing protocol. This basically is about using a different mounting medium. Easy, cheap and non-toxic! Give it a try!

Have a look at this post for more info.

Multiplexed immunostaining

It is not easy to find enough antibodies that work together to be able to label a sample with more than 4 antibodies at the same time. And even 4 is pushing it.

This paper describes a new immunostaining multiplexing method called 4i. The method is based on a special imaging buffer that prevents the antibody from being strongly bound to the sample due to the imaging process. This allows the authors to detach the antibody with gentle treatments, leaving the sample in a good shape and ready for another round of labeling and imaging.

Using this method they have successfully labelled the same sample with 40 different primary/secondary of the shelf antibody pairs!

How to precisely measure the volume of a cell?

Measuring the volume of a cell is often done by labelling the cell membrane or its cytoplasm. Analysing large flat cells this way is easy but it is much harder for tiny cells like blood cells, yeast or bacteria.

Another way to measure volumes is to use a negative stain, i.e. where the medium is made fluorescent with a dye that does not go into the cell. The cell appears as a black hole in fluorescent images and unlike lipid-based membrane labelling, borders are even and easy to segment.

While many dyes can be used for live cells, one must choose large dyes when negatively imaging cells that have been fixed and permeabilized.

This paper and this one use high molecular weight (2000 KDa) Dextran to achieve these results and measure the size of bacteria.

This recent paper optimizes the technique.

 

Deep red fluorescent proteins

The microscopy field is moving away from blue dyes. This is because red light, used to excited far red and deep red fluorophores, is less damaging to live cells than near UV light which is used to excite blue fluorophores.

On top of that, red light penetrates deeper into thick samples.

So as the trend in microscopy is to move to thicker samples and use more live samples, far red and deep red fluorophores are becoming more attractive.

Here is an article describing 3 new fluorescent protein in the far red to deep red range. One can excite them with 640 nm or a 685 nm lasers or LEDs.

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