MLE of the covariance matrix of a multivariate Gaussian distribution

I am reading through the following question: MLE of bivariate normal distribution

But there is one step I don't understand in the derivation of of the MLE for the covariance matrix:

$\Rightarrow \frac{\partial}{\partial\Sigma}\log f(X|\mu,\Sigma)=-\frac{n}{2}(\Sigma^{-1})^T-\frac{1}{2}\sum_i \frac{\partial}{\partial\Sigma}tr((X_i-\mu)(X_i-\mu)^T\Sigma^{-1})$

With some abuse of notation: $\Rightarrow \frac{\partial}{\partial\Sigma}\log f(X|\mu,\Sigma)=-\frac{n}{2}(\Sigma^{-1})^T-\frac{1}{2}\sum_i \frac{1}{\partial\Sigma}tr((X_i-\mu)(X_i-\mu)^T\partial\Sigma^{-1})$

$\partial\Sigma^{-1}=-\Sigma^{-1}\partial\Sigma\Sigma^{-1}$, by substitution:

$\Rightarrow \frac{\partial}{\partial\Sigma}\log f(X|\mu,\Sigma)=-\frac{n}{2}(\Sigma^{-1})^T-\frac{1}{2}\sum_i \frac{1}{\partial\Sigma}tr((X_i-\mu)(X_i-\mu)^T(-\Sigma^{-1}\partial\Sigma\Sigma^{-1}))$

$=-\frac{n}{2}(\Sigma^{-1})^T+\frac{1}{2}\sum_i \frac{1}{\partial\Sigma}tr(\Sigma^{-1}(X_i-\mu)(X_i-\mu)^T\Sigma^{-1}\partial\Sigma)$

$=-\frac{n}{2}(\Sigma^{-1})^T+\frac{1}{2}\sum_i (\Sigma^{-1}(X_i-\mu)(X_i-\mu)^T\Sigma^{-1})^T$

$\Rightarrow \frac{\partial}{\partial\Sigma}\log f(X|\mu,\Sigma)=-\frac{n}{2}(\Sigma^{-1})^T+\frac{1}{2}\sum_i (\Sigma^{-1}(X_i-\mu)(X_i-\mu)^T\Sigma^{-1})^T=0$

$\frac{1}{2}\sum_i (\Sigma^{-1}(X_i-\mu)(X_i-\mu)^T\Sigma^{-1})^T=\frac{n}{2}(\Sigma^{-1})^T$

The step that I don't understand is the step where the partial derivative dissapears. So $\partial$ is moved in the trace operator, then some manipulation is done on $\Sigma^{-1}$ and then somehow, at least that is how I see it, $\partial \Sigma$ is pulled out of the trace in order to get rid of the partial derivative? Can someone tell me why you can do this and why this is valid?

1. $\text{tr}(ABC) = \text{tr}(BCA) = \text{tr}(CAB)$ as along as the dimensions of matrices is in align with matrix multiplication.
2. $\frac{\partial \text{tr}(AX)}{\partial X} = A^{T}$