Category Archives: in-focus

IN FOCUS: Standard Operation Procedures (SOP) Repository.

Above: A screenshot of the Bioimaging Hub’s SOP repository

If you wasn’t already aware of the Bioimaging Hub’s SOP repository (N.B. there are shortcuts set up on all of the networked PCs within the facility), then please take a look at your earliest convenience. The database was set up as a wiki to provide Hub users with up to date protocols and tutorials for all of our imaging systems, experimental guidelines for sample preparation, health and safety information in a variety of multimedia formats in one convenient and easily accessible location. It’s still  work in progress and we would welcome any feedback on how the resource could be further developed or improved.

AJH 7.1.19

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.

Marc Isaacs, Bioimaging Technician

IN FOCUS: Cutting Through the Fog: Reducing Background Autofluorescence in Microscopy.

Autofluorescent bone sample

Above: Autofluorescence from mixed connective tissues imaged by confocal microscopy (left). The autofluorescent emissions can be spectrally-resolved through wavelength scanning (right). Excitation at 488nm.

Whilst autofluorescence from endogenous fluorophores can reveal much about the biochemical composition of a sample, it can also hamper the microscopic detection of targeted fluorochromes if they emit light at the same wavelengths as endogenous fluors. Indeed, without proper controls, complex background autofluorescence can lead to misinterpretation of image data and generation of false positive results.

Autofluorescence derives from multiple sources within the sample – the main culprits are  NADH and NADPH, lipofuscins, flavins, elastin and collagen (and lignin and chlorophyll in plants). The excitation and emission ranges of the worst offenders have been shown below. It follows that tissues with high collagen and elastin contents, e.g. skin, tendon and cartilage, autofluoresce very brightly; as do tissues that are rich in metabolic breakdown products such as lipofuscin, e.g. liver, spleen etc.

Autofluorescent data

Adding to the problem is the effect of chemical fixatives (e.g. formalin, glutaraldehyde etc) and solvents used to preserve tissue architecture for microscopy: the cross-linkages generated by these chemicals increase autofluorescence, which can be worsened further by long-term storage of the fixed processed tissues.

So, dear reader, here’s some simple advice on steps that you can take to address this common problem:

1. Include an unlabelled control to evaluate the level of autofluorescence within your sample.

  • Observation of unlabelled samples through RGB fluorescent filters (note their transmission characteristics) will help identify where in the visible spectrum the autofluorescent signal is brightest.
  • Spectral (lambda, wavelength) scanning will allow you to precisely identify the fluorescent emission spectra from endogenous fluorochromes and can help separate their emissions from those of your fluorochrome (see above figure).

2. Select fluorochromes that are outside the range of the autofluorescence.

  • If the autofluorescence signal is high in the blue, then move into the green; if it’s high in the green, move into the red – or better still, the far red (if your system can detect in this range).
  • Use modern fluorescent probes (e.g. Alexa Fluor, Dylight, or Atto range) instead of first generation fluorochromes.  They are brighter, more photo-stable and have narrower excitation and emission bands. They are also available in variants that span the near UV, visible and far red range of the spectrum, affording you plenty of choice.

3. Use a microscope with filters optimised for your choice of fluorochromes.

  • Band-pass filters which collect emissions within a specific range may be more useful than long-pass filter sets which collect all emissions past a certain wavelength. The narrower the range of the band-pass filter, then the better it can separate fluorophores with close emission spectra.

4. If the autofluorescence is unevenly distributed within your sample, use targeted microscopy to avoid it.

5. If you can’t avoid the autofluorescence, then take measures to remove or reduce it.

  • Analyse the pixel intensity distribution within your image and try thresholding out the lower intensity autofluorescence signal.
  • Pre-bleach your samples in a light box using a high intensity illumination source prior to fluorescent labelling (see below reference)
  • Treat samples with a chemical reagent (e.g. sodium borohydride, Sudan black B, ammonium ethanol etc) to reduce background autofluorescence (see below reference)

6. If all else fails, consider the following:

  • use cryoprocessed material as an alternative to chemical fixation and paraffin wax processing.
  • avoid long term storage of material/archival tissue samples.
  • try a different detection modality (e.g. immunoperoxidase instead of immunofluorescence)


Further reading

Wright Cell Imaging Facility. Autofluorescence: Causes and Cures


IN-FOCUS: Better To Burn Bright Than To Fade Away: Reducing Photo-bleaching in Fluorescence Microscopy.

[Parameter-Settings] FileVersion = 2000 Date/Time = 0000:00:00 00:00:00 Date/Time + ms = 0000:00:00,00:00:00:000 User Name = TCS User Width = 1032 Length = 1032 Bits per Sample = 8 Used Bits per Sample = 8 Samples per Pixel = 3 ScanMode = xy Series Name = demo2.lei

Above: Photo-bleaching (fading) occurs when a fluorochrome permanently loses the ability to fluoresce due to photon-induced chemical damage and covalent modification. 

Hands up if you’ve spent hours preparing a sample for fluorescence microscopy only to see the signal disappear before your eyes upon excitation? Frustrating eh (unless, of course, FRAP is your objective)? Well here’s some simple and sound advice on how you can minimise photo-bleaching and get the best out of your samples under the fluorescence microscope.

1. Visualise your samples immediately after fluorescent labelling – this is when they are at their brightest.

  • If this is not possible then loosely wrap your samples in aluminium foil and keep them in the dark at 4oC until you get the opportunity to image them.

2. Minimise their exposure to light in order to reduce photo-bleaching.

  • visualise your samples under low light conditions.
  • use transmitted light to find a region of interest (ROI) and then switch to epifluorescence observation – avoid dwelling too long on the ROI.
  • step down the intensity level of excitation light or insert a neutral density filter into the light path.
  • set up imaging parameters on a neighbouring region and then return to the ROI for image capture.
  • use image binning to reduce exposure time.
  • use the microscope shutter to switch off the light source between images.
  • create a photo-bleach curve from a timed series of images. This can be used to normalise for loss of fluorescence intensity.

3. Switch to a mounting medium with anti-fade protection e.g. Vectashield, Prolong Gold/Diamond, SlowFade Gold/Diamond. These work by reducing the oxygen available for photo-oxidation reactions, thus reducing photo-bleaching. N.B. Many of these are available with a nuclear counterstain (e.g. Dapi) included in the formulation. Alternatively, make your own anti-fade reagent (instructions below).

4. Switch to brighter, more photo-stable fluorochromes. First generation fluorochromes such as FITC and TRITC photo-bleach readily (and are pH sensitive) thus should be replaced with modern dyes such as the Alexa Fluor, Dylight, or Atto  range of fluorochromes, which are much brighter and far more photo-stable.

Good luck!



Further reading

IN-FOCUS: Development of a 3D printed pollen reference collection.

pollen montage 1
pollen montage 2

Above: surface-rendered confocal reconstructions of pollen samples (left) and their corresponding 3D printed models (right).

Isn’t the World Wide Web a wonderful thing? Not so long ago I wrote a short blog explaining how we had developed methodology to convert volume datasets from the confocal microscope into 3D printed models – perfect solid scale replicas of samples the size of a pollen grain etc. Well, shortly afterwards I received an email from someone who had not only read the blog but, serendipitously, wanted to do this very thing! What is more, she was located not a million miles away: in fact, little more than 400 yards down the road from us, working as a researcher within Cardiff University’s School of History, Archeology & Religion. Please excuse the pun, but it really is a small world!

Rhiannon Philp is an archaeologist – or palynologist to be precise – someone who studies ancient pollen grains and spores found at archaeological sites. Pollen extracted from archeological digs can be used for radiocarbon dating and for studying past climates and environments by identifying plants growing at the time. Rhiannon is using this information to develop an understanding of prehistoric sea level changes in South Wales as part of the Changing Tides Project.

Rhiannon asked if we could generate a reference collection of 3D pollen prints that could be used for teaching and outreach activities as part of a new Archaeology engagement project called Footprints In Time. Indeed, some of her pollen samples were from sites containing both human and animal footprints made over 5000 years ago!

You can see some of our results above: on the left are the surface-rendered confocal volume reconstructions and, on the right, their corresponding 3D printed facsimiles – courtesy of the BIOSI 3D printing facility.

If you’re at the National Eisteddfod in Abergavenny this week (29th July – 6th August), then please pop by to see Rhiannon’s stall within the Cardiff University tent – all of the models will be on display there, together with a lot more.  Any further interest, then please get in touch.


 Further reading:

IN-FOCUS: Bigging it up: 3D printing to change the shape of microscopy.

3d pollen

Virtual to reality: a surface-rendered digital image of a single pollen grain generated by confocal microscopy (left) is 3D printed into a 2000x scale replica model (centre & right).

Imagine being able to generate a highly accurate, solid scale replica of the sample that you are visualising down the microscope; a perfectly-rendered pollen grain, or blood cell, or microscopic organism, but big enough to hold and examine in your hand.  It would allow much better 3D conceptualisation of the sample, particularly for blind or visually-impaired individuals, and would have enormous utility in teaching and in engagement activities, and what researcher wouldn’t want a tangible, physical embodiment of their research to help explain their work (and impress their colleagues) at scientific meetings? Sounds like the stuff of science fiction doesn’t it? Well, not any more. Thanks to 3D printing technology (and the help of Dr Simon Scofield‘s lab) we have started taking volume datasets from the confocal microscope out of the virtual world and making them a reality. If you would be interested in generating a highly accurate scale model of your favourite biological sample (or would simply like to handle a giant pollen grain!) then please feel free to get in touch.


 Further reading:

IN-FOCUS: Microscopy on the move. A round-up of the best microscopy apps for mobile devices.

mobile microscope

Here’s a quick round up of some useful imaging applications for portable Android and Apple devices.

  • Molecular Probes 3D Cell App. Learn about the cell and all its structures in 3D on Apple portable devices. Enjoy the ability to rotate the cell 360 degrees and zoom in on any cell structure.

If you wish to use your smartphone camera as a rudimentary digital magnifier just search ‘microscope’ in either the Google Play or iTunes App stores – there’s loads to choose from. Instructions available here showing how to build a perspex support stage with transmitted light illumination for your smartphone. If you want anything more sophisticated, take a look at this.


IN-FOCUS: Microscopy and Analysis Journal: A Useful Resource for Microscopists.

I can see why Microscopy and Analysis is the leading international journal for microscopists – it’s  chock-full of interesting articles, features and news on all things related to microscopy and imaging. More to the point, it’s free to individuals who purchase, specify or approve microscopical, analytical and or/imaging equipment at their place of work. The journal is published six times per year, in January, March, May, July, September, and November. There are also several supplements published periodically, which include publications devoted to special events, trade shows and specific areas of microscopy and imaging. The journal is available in print,  or can be viewed online in an interactive format, or via a downloadable app. We also have lots and lots of back issues available within the Bioimaging Unit, which you are welcome to peruse on your next visit!


IN-FOCUS: Imaging on a Budget? A Round-up of the Best Free Imaging Software on the Web.

Grant failed to make it past triage? Departmental account looking decidedly bare? Fear not dear reader, we have trawled the net to come up with a list of the best free imaging software out there…

The following links are to downloads of free software for image acquisition, processing and multi-dimensional analysis. Hardware requirements, application notes and user instructions are all available through the individual websites. Please note that some of the downloads will require site registration.

BioImageXD    Open source software for analysing, processing and visualising multi-dimensional microscopy images.

Cell Profiler    Versatile 2D processing platform for high throughput screening applications.

Confocal Assistant    Software for 3D processing and analysis of confocal images.

Drishti    Advanced software for 3D rendering of volumetric datasets.

FluoRender    Interactive 3D rendering tool for confocal microscopy designed specifically for neurobiologists.

Icy  open community platform for bioimage informatics. Broad selection of plugins and protocols.

ImageJ    Multi-format (Java-based) open source software package for data acquisition, analysis and processing. Extensive functionality conferred via a wide selection of downloadable plugins.

LAS-AF Lite    ‘Lite’ version of the Leica application suite which allows basic processing and analysis of  image data obtained from advanced Leica widefield and confocal systems.

LCS Lite    ‘Lite’ version of the Leica confocal software that allows basic processing and analysis of Leica SP2 confocal image files.

Micro-Manager    Open source software for control of automated microscopes which runs as a plugin to Image J.

Open Microscopy Environment (OMERO)    Client server software for visualisation, management and analysis of biological images.

DeconvolutionLab    Software for  deconvolution of 2D or 3D microscopic images which runs as a plugin to Image J.

V3D    Powerful open source software for visualisation and segmentation of large 3D datasets.

View5D    Software for analysis and processing of multi-dimensional volumetric datasets which runs as a plugin to Image J.

VisBio    Open source software for visualisation and analysis of multidimensional image data. Interfaces with Image J and OMERO.

Voxx    Voxel-based rendering software for 3D analysis of confocal and multi-photon datasets.

LSM image browser    Imaging software for Zeiss LSM 5 series confocals.

ZEN Lite    ‘Lite’ version of the Zeiss Efficient Navigation  (ZEN) software that allows basic processing and analysis of image data from advanced Zeiss light microscopical systems.


IN-FOCUS: De-boning the Zebrafish: unpicking skeletogenesis under the microscope.

Confocal reconstructions of the head, thorax and tail regions of the Zebrafish (Danio rerio)

Confocal reconstructions of the head, thorax and tail regions of the Zebrafish (Danio rerio)

The Zebrafish (Danio rerio) is, in many ways, the perfect model for microscopists. Not only does it share 70% genetic homology with man, but its larvae are born in large, transparent broods all year round and develop extremely quickly (a single cell develops into something resembling a fish within 24 hours!)  This means that developmental events can be visualised in vivo in real-time down the microscope. On top of this, their genome has been sequenced and it is easily amenable to molecular manipulation- again, these manipulations can be followed closely under the microscope lens.

Over the last few years we have been collaborating with Dr Chrissy Hammond at Bristol University, a fish biologist who shares an interest in skeletal development and disease. In our joint studies, we have used a variety of imaging techniques (brightfield, DIC, polarising, epifluorescence, confocal, TEM, radiography and microCT) to investigate skeletal development, growth and ageing in  this animal model.

One of the many interesting findings from our studies is that ageing fish undergo degenerative changes to their spine that resemble osteoarthritis (for example, spinal curvature, osteophyte formation, and connective tissue degeneration). This opens up the possibility  that they could be used to experimentally model aspects of the human disease. So it’s not just fishy tails!


Find out more:
Further Reading: