Author Archives: Marc Isaacs

About Marc Isaacs

Laboratory technician for the Bioimaging and Histology units within the School of Biosciences.

IN FOCUS: Imaging Cleared Tissues by Lightsheet Microscopy.

We’ve had a Zeiss Lightsheet Z.1 system in the Bioimaging Hub for a little while. In the main, the system  has been used to examine small developmental organisms (e.g. zebrafish larvae) and organoids that can be introduced into the lightsheet sample chamber via thin  glass capillary tubes (0.68-2.15mm diameter) or via a 1 ml plastic syringe.  This is accomplished by embedding the sample in molten low melting point agarose, drawing it into the capillary tube/syringe and then, once the agarose has set,  positioning the sample into the light path by displacing the solid agarose cylinder out of the capillary/syringe via a plunger.

To support a new programme of research, the Bioimaging Research Hub recently purchased a state of the art  X-CLARITY tissue clearing system. This allows much larger tissue and organ samples to be rendered transparent quickly, efficiently and reproducibly for both confocal and lightsheet microscopy. Unfortunately, due to their larger size, the samples cannot be introduced into the lightsheet sample chamber via the procedure described above.  In this technical feature we have evaluated a range of procedures for lightsheet presentation of large cleared mammalian tissues and organs.

The test sample we received in PBS had been processed  by a colleague using the X-CLARITY system using standard methodologies recommended by the manufacturer. The tissue was completely transparent  after clearing, however its transferal to PBS (e.g. for post-clearing immuno-labelling) resulted in a marked change in opacity, the tissue becoming cloudy white in appearance. We thus returned the sample to distilled water (overnight at 4oC) and observed a return to optical clarity with slight osmotic swelling of the tissue.

Above: The cleared sample has a translucent, jelly-like appearance.

Generally, cleared tissue has a higher refractive index than water (n=1.33) and  X-CLARITY tissue clearing results in a refractive index close to 1.45. To avoid introducing optical aberrations that can limit resolution,  RI-matching of substrates and optics is recommended.  Consequently, our plan was to transfer the cleared tissue into X-CLARITY RI-matched (n=1.45) mounting medium and set up the lightsheet microscope for imaging of cleared tissues using a low power x5 detection objective (and x5 left and right illumination objectives) which would allow us to capture a large image field. 

Prior to fitting the x5 detection objective an RI-matched spacer ring (see below) was first screwed into the detection objective mount.

Above: Spacer ring for n=1.45 lenses.

After the n=1.45 spacer ring was fitted, the x5 detection objective was screwed into place (seen centrally in below image) followed by the x5 illumination objectives to the left and right (see below).

Above: Light sheet objectives. Illumination on left and right, observation to the rear.

Once the objective lenses had been screwed into place, the sample chamber was inserted (see below). The use of a clearing mountant requires a specific n=1.45 sample chamber. We used the n=1.45 chamber for the x5 (air) detection objective. This chamber has glass portals (coverslips) on each of its  vertical facets (unlike the x20 clearing chamber that is open at the rear to accomodate the x20 detection lens designed for immersion observation).

Above: Sample chamber for clearing (n=1.45).

Unfortunately, as it turned out, the RI-matched X-CLARITY mounting medium for optimum imaging of X-CLARITY cleared samples wasn’t available to us on the day, necessitating a quick re-think. As the tissue sample remained in distilled water  we decided to image, sub-optimally, in this medium. To do this we quickly swapped the n=1.45 spacer ring on the detection objective to the n=1.33 spacer. We then switched to the standard (water-based) sample chamber.

With the system set-up for imaging we set about preparing the tissue sample for presentation to the lightsheet.  As mentioned earlier, large tissue samples cannot be delivered to the sample chamber from above, as the delivery port of the specimen stage has a maximum aperture of 1cm across. This necessitates (i) removing the sample chamber (ii) introducing the specimen holder into the delivery port, (iii) manually lowering the specimen stage into place (iv) attaching the sample to the specimen holder (v) manually raising the specimen stage, (vi) re-introducing the sample chamber, and then (vii) carefully lowering the specimen into the sample chamber which can then (viii) be flooded with mounting medium.

The initial idea was to present the sample to the lightsheet, as described above, by attaching it to a 1ml plastic syringe. The syringe is introduced into the delivery port of the lightsheet via a metal sample holder disc (shown below).

Above: Holder for 1mm syringe.

The syringe is centred in the sample holder disc via a metal adaptor collar (shown below), which must be slid along the syringe barrel all the way to its flange (finger grips). When we tried this using the BD Plastipak syringes supplied by Zeiss we found that the barrels were too thick at the base so that the the collar would not sit flush with the flange!

Above: Metal collar for the syringe holder.

In order to make it fit, we carefully shaved off the excess  plastic  at the base using a razor blade.

Above: Carefully shaving plastic off the syringe to make it fit.

This allowed the adaptor collar to be pushed flush against the barrel flange (see below).

Above: Syringe with the adaptor collar in the correct position.

The flanges themselves also required a trim as they were too long to position underneath the supporting plates of the specimen holder disc. Note to self: we must find another plastic syringe supplier!

Once the syringe had been modified to correctly fit the sample holder it was introduced into the delivery port of the specimen stage, in loading position, by aligning the white markers  (see below)   

Above: Syringe plus holder inserted into the delivery port of the sample stage (note correct alignment of white markers).

With the front entrance of the lightsheet open and the sample chamber removed, the stage could be safely lowered via the manual stage controller with the safety interlock button depressed (see below). 

Above: Button for safety interlock under the chamber door.

The stage was lowered so that the syringe tip was accessible from the front entrance of the lightsheet (see below).

Above: Syringe dropped down to an accessible position.

Our first thought was to impale the tissue sample onto a syringe needle so that it could then be attached to the tip of the syringe (see below).

Above: Using a needle to impale the sample.

However, this approach failed miserably as the sample slid off the needle under its own weight.  In an attempt to resolve this problem, a hook was fashioned from the needle in the hope that this would support the weight of the tissue (see below).

Above: A pair of pliers was used to bend the needle and make a hook.

Unfortunately, this approach also failed, as the hook tore through the soft tissue like a hot knife through butter.

We decided therefore to chemically bond the tissue to one of the short adaptor stubs included with the lightsheet system with super-glue. The adaptor stubs can be used with the standard sample holder stem designed for capillary insertion. They attach to the base of the stem via an internal locking rod with screw mechanism (shown below).

Above: Sample stub for glued samples.

To introduce the sample holder stem into the sample chamber (see below) we used essentially the same process as that described above for the syringe.

Above: The sample holder stem being  lowered into position (the central locking rod can be seen protruding out)

The tissue sample was carefully super-glued on to the adaptor stub for mounting onto the sample holder stem.

Above: Super-gluing the sample on to the adaptor stub.

Again, under its own weight, the sample tore off the stub leaving some adherent surface tissue behind (see below)

Above: Adaptor stub with tissue torn off.

It seemed pertinent at this stage to reduce the sample volume as  it was clearly the weight of the tissue that was causing it to detach. Having already visualised the sample under epifluorescence using a stereo zoom microscope we had a very good idea of where the fluorescent signal was localised in the tissue. Consequently, we  reduced the sample to approximately one third of its original size and again glued it to the sample stub ensuring that the region of interest would be accessible to the lightsheet (see below).

Above: Sample cut down to size and attached successfully to stub.

This time it held. The adaptor stub was then carefully secured to the sample holder stem via its locking rod, the stage manually raised  and the sample chamber introduced into the lightsheet. The sample was manually lowered  into position so that it was visible through the front viewing portal of the sample chamber. The sample chamber was then carefully filled with distilled water for imaging (remember, we didn’t have any X-CLARITY mounting medium at this stage).

Above: Sample positioned in the imaging chamber.

With the sample in place, we then set up the lightsheet for imaging GFP fluorescence. Due to the large sample size, we found that we could only image from one side (the lightsheet couldn’t penetrate the entire sample without being scattered or attenuated). The sample was therefore re-oriented so that the region of interest was presented directly to the lightsheet channel coming in from the right. We then switched on the pivot scan to remove any shadow artefacts and set up a few z-series through the tissue. The image below shows the sort of resolution we was getting off the x5 detection objective using maximum zoom.

Above: Low power reconstruction of neuronal cell bodies in brain tissue. Dataset taken to a depth of 815 microns from the tissue surface  (snapshot of 3D animation sequence).

It took us a fair amount of time to establish a workflow for the correct preparation and presentation of the sample to the lightsheet.  However, once we had established this we were able to get some pretty good datasets and in very good time – the actual imaging part was relatively straightforward. The next step will be to repeat the above using the refractive-index matched X-CLARITY mounting medium,  try out the x20 clearing objective and utilise the multi-position acquisition feature of the software.

Contributions:

Tissue clearing and labelling, I. M. Garay; preparation, presentation and lightsheet imaging of sample, A. J. Hayes; photography, M. Isaacs; text, M. Isaacs and A.J.Hayes.

Further reading:

 

IN-FOCUS: Making Imaris a Bit(plane) Faster

Most of the work of the Bioimaging Hub is concentrated on acquiring images – choosing the right equipment, optimising settings, advising about sample prep, etc. We do, however, have a few systems dedicated to analysing images too. We’ve got a system running Arivis Vision4D which specialises in extremely large datasets, such as Lightsheet data, as well as a system running Bitplane Imaris. We’ve had Imaris for longer and so it’s seen a lot more use. This was recently updated with a Filament Tracer module for analysing neuronal processes. Shortly after this upgrade was added we experienced a severe slowdown in the software. It would take over a minute to go from the ‘Surpass’ view, where images are analysed, to the ‘Arena’ view, where files are organised. The information for the files in Arena is stored in a database and we suspected that the database was at fault.

Imaris hanging when switching from Surpass to Arena. It would do this for about 65 seconds every time a change was made.

A call with Imaris technical support was arranged and the software examined. There were no apparent errors in any of the logs and the database was functioning as it should. The only advice available was to thin down the number of entries in the database – we were told it had nearly 240,000 entries which, even accounting for metadata, seemed vastly excessive for the number of files in Arena.

Complete database dump of nearly 240,000 entries.

I decided to try to trim the database.

My first thought was the Filament Tracer module was generating a large amount of statistics and these were being stored in the database. A couple of users had been experiencing crashes when accessing these statistics so it was possible that slow database responses were bringing the software down. I backed up all datasets which used the Filament Tracer (a process far more laborious than it should be) and deleted them all from Arena. This dropped the database access time from 65 seconds to 60. Not that much of a result.

My next thought was that the co-ordinates for the vertices of surface renders might have been clogging up the database. We’ve generated quite a lot of 3D models of pollen grains and cells so this was potentially a lot of data. I went through the same laborious process of exporting all these datasets and deleted them. Again, little improvement.

I decided I needed to look at the database directly. The underlying database runs on PostgreSQL as part of the BisQue bioimaging package. Using the pgAdmin tool I began to browse the database to see where the data was held and how it was organised.

Structure of the underlying database.

I couldn’t find any trace of it so I exported the entire thing as a text file and loaded it into NotePad++. As Imaris technical support had told us, it was enormous – 55MB of text. Scanning the file, eventually I found that practically all the data was held in a database table named ‘taggable’. I’d skipped over this at first as the name was so nondescript.

Using Notepad++ to check a database dump and find where the data is stored – the table named ‘taggable’.

Once I knew all the data I needed was in this table I began to examine it. The first thing that jumped out at me was the huge number of entries in the database relating to datasets from our Leica confocal system. This system stores its data as a series of tif images, one per channel, one per z-position for z-stacks. Every single one of these files had its own database entry as a dependency for a ‘resource_parent_id’.

Database entries for Leica datasets. One record per channel, per z-position which becomes a huge number of entries.

A lot of old Leica datasets had been loaded into Arena recently to see if any new information could be extracted from them and this had massively inflated the size of the database. I exported all these datasets as new Imaris .ims files and deleted them from Arena. This reduced the number of database entries from just under 240,000 to just over 16,000. As a result the database access time dropped to about 18 seconds. Much more manageable but still a bit slow.

Looking at the database entries again, I could see that there were still lots of entries relating to Leica datasets. I went back to look at Arena but there was no sign of them. These were orphaned entries relating to non-existent data. As it was impossible to delete them from Arena, I identified all of their resource_parent_id numbers and used pgAdmin to delete them

Manually deleting orphaned database entries.

It then occurred to me that the indexes for the database were probably totally out of date so my final task to optimise things was the rebuild all of the indexes in pgAdmin

Rebuilding the table indexes.

All of these steps got the database access time down to 3 seconds – quite a bit improvement on the original 65 seconds! Importing some of the exported datasets as Imaris .ims files slowed it back down to about 10 seconds so it’s apparent that the database scales very poorly. Still a lot better than when the Leica datasets were numerous separate files though. It looks to me that the database design favours flexibility over scaling which ends up being not very useful if you want to use it to organise a reasonable amount of imaging data.

So if you’ve got Imaris database lag there’s a few things you can try. The main improvement was to make sure your datasets are represented by single files, either by exporting them as Imaris .ims files or converting them to something like OME-TIFF first.

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Marc Isaacs, Bioimaging Technician