Feeds:
Posts
Comments

Archive for the ‘Olfaction’ Category

In their paper released today, Jafari et al make major strides in answering this question. First, they systematically knocked down 611 transcription factors in Drosophila (~80% of the total) in four representative classes of olfactory sensory neurons. They identified seven whose loss led to a strong decrease in odorant receptor expression.

Next, they showed that knocking down at least one of these seven transcription factors in almost all of the known olfactory sensory neuron classes (32/34) caused that class to stop expressing its olfactory receptor.

rows = transcription factors, columns = olfactory sensory neuron classes; grey = wildtype-like expression, black = no expression, odorant receptor expression detected by ISH; orange = trichoid, one of the three major odorant receptor expression domains; note that the raw data in table s2 is a bit more noisy than the simplified version above, as expected; doi:10.1371/journal.pbio.1001280

In their discussion, the authors mention that the seven transcription factors they found is likely to be an underestimate. This makes sense because the library wasn’t available to screen every transcription factor, and RNAi is stochastic. Regardless, their data set and paradigm should open up many avenues for studying combinatorial transcriptional coding.

Reference

Jafari S, Alkhori L, Schleiffer A, Brochtrup A, Hummel T, et al. (2012) Combinatorial Activation and Repression by Seven Transcription Factors Specify Drosophila Odorant Receptor Expression. PLoS Biol 10(3): e1001280. doi:10.1371/journal.pbio.1001280

Read Full Post »

In mice, each olfactory neuron expresses exactly one of ~ 1000 types of olfactory receptors. Through combinatorial coding, the system is able to recognize a wide range of odors and their combinations. But how exactly is this diversity of responses achieved?

Nara et al. recently set out to answer this question. They put dissociated mice olfactory epithelium cells on glass coverslips, loaded them with a calcium indicator, and monitored them for changes in calcium signaling following the application of 13 different odorant mixtures.

OSN = olfactory sensory neuron, the actual number of neurons in each group is shown above each bar (out of a total of 217 tested); doi: 10.1523/ JNEUROSCI.1282-11.2011

As you can see above, most neurons responded (i.e., demonstrated an increased calcium concentration) to just one mixture, but some neurons did express receptors which allowed them to respond to many mixtures.

If a neuron responded to a given mixture, it was then tested for a response to each of the individual odorants in that mixture. Here is the response curve for one of their neurons, which responded to many different mixtures:

a sharp line indicates a change in fluorescence intensity, which is a proxy for neural activity; the bars indicate when the odors were added; the final response to KCl indicates neuron viability; Fi = change in fluorescence; doi: 10.1523/ JNEUROSCI.1282-11.2011

This neuron was considered an example of a “broad tuning,” since it responded to different odors with high structural variability.

Although all of the responses marked in red were classified as a “recognition,” some responses (such as 4-2) seem to be stronger than others (such as 6-10).

It might be interesting to analyze multiple replicates of responses from the same odor on the same neuron, to allow the authors to parse signal from noise and see whether these gradations in response are significant.

Cracking the full odorant code will require an even more high-throughput set of experiments, and probably would have to have at least one data point from each type of odorant receptor. This study is a clear proof of principle that such an extension would be possible and valuable.

Reference

Nara K, et al. 2011 A Large-Scale Analysis of Odor Coding in the Olfactory Epithelium. doi: 10.1523/​JNEUROSCI.1282-11.2011

Read Full Post »