New sperm research could lead to cut in infertility rate
Sperm and mathematics don’t appear to be the likeliest of bedfellows – but new research bringing the two together could lead to devices that could cut infertility rates.
With infertility affecting about one in six people, a team of mathematicians, bioengineers, computer engineers and clinicians are working on a system that could identify which sperm are able to successfully deliver their cargo of DNA to the egg.
The University of Birmingham research, funded through an Engineering and Physical Sciences Research Council Healthcare Technologies Challenge Award, could then lead to better treatment decisions that would save distress and expense and lead to more healthy births.
Infertility treatments such as IVF are currently hampered by imprecise diagnostics, with the monitoring of sperm not utilising cutting edge technology.
However that could soon change, with the team hopeful of creating a new system which would utilise phase-contrast imaging to observe sperm before analysing them mathematically.
This could then lead to a better way of identifying which sperm have the attributes required to successfully fertilise the egg, and an improvement in the advice provided to couples going through fertility treatment.
Project Lead Dr Dave Smith, from the School of Mathematics, University of Birmingham, says he hopes the work could lead to new equipment that could be used in andrology clinics to identify the condition of sperm and what treatment or lifestyle changes are required.
He said: “Unfortunately infertility is a common problem, with male infertility accounting for about half of all cases. The problem is that the diagnostic methods used at the moment are quite coarse and there aren’t good enough tools to deal with it either.
“That has a very big knock-on effect because IVF is very expensive, not everyone can have the treatment, and it can be very gruelling, especially for the female partner.
“We hope this could tell you not just core statistics about sperm such as swimming speed, but which ones have the ‘right stuff’ and which ones are swimming efficiently and are correctly formed.
“We want to provide a new way of looking at cells, what infertility is and what a cell should be able to do.
“The long-term impact could lead to a better use of resources for treatments such as IVF and hopefully improved success rates. We would be able to give people the right kind of advice on lifestyle choices, so for example if smoking is damaging sperm quality we can identify that and advise the patient accordingly.”
EPSRC’s Chief Executive, Professor Philip Nelson, said: “This research demonstrates the significance of the work being undertaken by the winners of our Healthcare Technologies Challenge Awards.
“The impact of the work could have a wide range of important implications, from allowing for better analysis of sperm to reducing the distress caused by infertility issues and improving the advice that can be offered to couples trying to conceive.”
“These Healthcare Technologies Challenge Award winners are our future research leaders who will be instrumental in ensuring the UK can meet the 21st century healthcare needs and thrive as a healthy nation.”
1. What does this equation help represent or identify the perfect sperm and how efficient it is?
The equation measures how efficiently a sperm moves – the top of the equation measures velocity produced by the movement of the tail, the bottom of the equation is how fast the sperm is using energy. A sperm needs to have both good ‘miles per gallon’ and also good speed – like a turbo diesel!
A big part of the research is developing computer methods to automatically recognise and accurately capture the movement of the tails and shape of the head so that the above equation – and other measures of sperm quality – can be worked out by an app. The tails beat very fast and have quite complex shapes – if you look at sperm under a microscope the tail is just a blur.
2. How can it be applied in real life scenario, ie, to increase fertility chances?
Male factors are present in about half of all infertility cases. The main purpose of the project is to improve the diagnosis of male infertility. ‘Sperm counting’ is generally done by eye and is subject to human error. What we want to do is to tell whether a man has enough sperm which can make the long and difficult journey to the egg in natural conception, whether intra-uterine insemination (which drops the sperm off near the egg) is sufficient, or whether more invasive treatments like IVF and ICSI (injection of a sperm into an egg) are needed. Doctors want to avoid more invasive treatments if possible, and also to make faster and more accurate decisions about what the right sort of treatment for a couple are, improving success rates and saving patients distress and expense.
Another appealing prospect of this type of high tech diagnosis is that we should be able to detect where lifestyle changes (smoking cessation, diet, vitamins) can make a difference to sperm quality – giving an evidence-based reason for lifestyle changes that could avoid the need for fertility treatment at all. It could also help research into new drugs to make sperm swim more effectively.
3. Why is it hard to identify good quality sperm?
Sperm have two vital jobs to do: they need to be good swimmers, and they need to have safely-packaged DNA which will form the basis for half of the genetic material in every cell of the resulting baby. Existing techniques can measure numbers, swimming, shape and even DNA damage (although the latter hasn’t reached wide clinical use yet). But these techniques can’t measure all of these aspects in the same sperm at the same time! It is no good having good swimmers with damaged DNA, or vice-versa. Human sperm are also quite challenging to work with because of the huge variability in samples and low numbers of ‘normal’ sperm.
Male fertility diagnosis has perhaps not kept pace with other higher profile areas of medical technology, but we are now in a position to change this. Our project brings together advances from across science – pattern recognition, camera technology (think of the quality of the camera in your smartphone), special imaging methods such as fluorescence, and modelling of energy efficiency (as used in aeroplane design for example), all brought together with maths.